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21 - How Should One Measure “Outcome” of Concussion?: An Introduction to the Common Data Elements for Mild TBI and Concussion

from Part III - Diagnosis and Management of Concussion

Published online by Cambridge University Press:  22 February 2019

Jeff Victoroff
Affiliation:
University of Southern California, Torrance
Erin D. Bigler
Affiliation:
Brigham Young University, Utah
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Summary

More than a decade before the publication of this text, leaders at the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) in Bethesda, Maryland, judged that research might be better coordinated, and efforts of different teams of investigators far better synthesized, if scholars used a common toolbox of outcome measures. The importance of that judgment is apparent to anyone who has undertaken a meta-analysis only to find that 100 experiments seeking to test the same scientific hypothesis were all done in unique and incomparable ways. The initiative to rectify this problem by strongly recommending specific instruments was christened the Common Data Elements (CDE) project. The authors of the present chapter have been intimately engaged in this progressive initiative. Here, they expertly summarize the first major product released by that team: the CDEs for traumatic brain injury. This is half the battle toward capitalizing on the collective efforts of researchers around the nation and the world, all working to understand CBI. The other half of the battle, of course, is to define CBI biologically.
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Concussion and Traumatic Encephalopathy
Causes, Diagnosis and Management
, pp. 694 - 715
Publisher: Cambridge University Press
Print publication year: 2019

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References

Thompson, HJ, Vavilala, MS, Rivara, FP. Common data elements and federal interagency traumatic brain injury research informatics system for TBI Research. Annu Rev Nurs Res 2015; 33: 111.Google Scholar
Saatman, KE, Duhaime, AC, Bullock, R, Maas, AI, Valadka, A, Manley, GT, et al. Classification of traumatic brain injury for targeted therapies. J Neurotrauma 2008; 25: 719–738.CrossRefGoogle ScholarPubMed
Thurmond, VA, Hicks, R, Gleason, T, Miller, AC, Szuflita, N, Orman, J, et al. Advancing integrated research in psychological health and traumatic brain injury: Common data elements. Arch Phys Med Rehabil 2010; 91: 16331636.CrossRefGoogle ScholarPubMed
Duhaime, AC, Gean, AD, Haacke, EM, Hicks, R, Wintermark, M, Mukherjee, P, et al. Common data elements in radiologic imaging of traumatic brain injury. Arch Phys Med Rehabil 2010; 91: 16611666.Google Scholar
Maas, AI, Harrison-Felix, CL, Menon, D, Adelson, PD, Balkin, T, Bullock, R, et al. Common data elements for traumatic brain injury: Recommendations from the interagency working group on demographics and clinical assessment. Arch Phys Med Rehabil 2010; 91: 16411649.Google Scholar
Manley, GT, Diaz-Arrastia, R, Brophy, M, Engel, D, Goodman, C, Gwinn, K, et al. Common data elements for traumatic brain injury: Recommendations from the biospecimens and biomarkers working group. Arch Phys Med Rehabil 2010; 91: 16671672.Google Scholar
Haacke, EM, Duhaime, AC, Gean, AD, Riedy, G, Wintermark, M, Mukherjee, P, et al. Common data elements in radiologic imaging of traumatic brain injury. J Magn Reson Imaging 2010; 32: 516543.CrossRefGoogle ScholarPubMed
Wilde, EA, Whiteneck, GG, Bogner, J, Bushnik, T, Cifu, DX, Dikmen, S, et al. Recommendations for the use of common outcome measures in traumatic brain injury research. Arch Phys Med Rehabil 2010; 91: 1650–1660 e17.Google Scholar
Whyte, J, Vasterling, J, Manley, GT. Common data elements for research on traumatic brain injury and psychological health: Current status and future development. Arch Phys Med Rehabil 2010; 91: 16921696.CrossRefGoogle ScholarPubMed
Hicks, R, Giacino, J, Harrison-Felix, C, Manley, G, Valadka, A, Wilde, EA. Progress in developing common data elements for traumatic brain injury research: Version two – The end of the beginning. J Neurotrauma 2013; 30: 18521861.Google Scholar
Adelson, PD, Pineda, J, Bell, MJ, Abend, NS, Berger, RP, Giza, CC, et al. Common data elements for pediatric traumatic brain injury: Recommendations from the working group on demographics and clinical assessment. J Neurotrauma 2012; 29: 639–53.CrossRefGoogle Scholar
Bell, MJ, Kochanek, PM. Pediatric traumatic brain injury in 2012: The year with new guidelines and common data elements. Crit Care Clin 2013; 29: 223238.CrossRefGoogle ScholarPubMed
Berger, RP, Beers, SR, Papa, L, Bell, M. Common data elements for pediatric traumatic brain injury: Recommendations from the biospecimens and biomarkers workgroup. J Neurotrauma 2012; 29: 672677.CrossRefGoogle ScholarPubMed
Duhaime, AC, Holshouser, B, Hunter, JV, Tong, K. Common data elements for neuroimaging of traumatic brain injury: Pediatric considerations. J Neurotrauma 2012; 29: 629633.Google Scholar
Gerring, JP, Wade, S. The essential role of psychosocial risk and protective factors in pediatric traumatic brain injury research. J Neurotrauma 2012; 29: 621628.Google Scholar
Hunter, JV, Wilde, EA, Tong, KA, Holshouser, BA. Emerging imaging tools for use with traumatic brain injury research. J Neurotrauma 2012; 29: 654671.CrossRefGoogle ScholarPubMed
McCauley, SR, Wilde, EA, Anderson, VA, Bedell, G, Beers, SR, Campbell, TF, et al. Recommendations for the use of common outcome measures in pediatric traumatic brain injury research. J Neurotrauma 2012; 29: 678705.Google Scholar
Miller, AC, Odenkirchen, J, Duhaime, AC, Hicks, R. Common data elements for research on traumatic brain injury: Pediatric considerations. J Neurotrauma 2012; 29: 634638.Google Scholar
Wechsler, D. Wechsler Abbreviated Scale of Intelligence – Second edition. Bloomington, MN: Pearson, 2011.Google Scholar
Delis, D, Kramar, J, Kaplan, E, Ober, B. California Verbal Learning Test – Children’s version. San Antonio, TX: Pearson Assessments, 1994.Google Scholar
Goodman, A, Delis, D, Mattson, S. Normative data for four-year old children on the California Verbal Learning Test-Children’s version. Clin Neuropsychol 1999; 13: 274282.Google Scholar
Rosselli, M, Ardila, A, Bateman, J, Guzman, M. Neuropsychological test scorse, academic performance, and developmental disorders in Spanish-speaker children. Dev Neuropsychol 2001; 20: 355373.Google Scholar
Delis, D, Kaplan, E, Kramer, J. Delis-Kaplan executive function system examiner’s manual. San Antonio, TX: NCS Pearson, 2001.Google Scholar
Wechsler, D. WISC-IV administration manual. San Antonio, TX: Pearson Assessments, 2003.Google Scholar
Wechsler, D. WISC-IV technical and interpretive manual. San Antonio, TX: Pearson Assessments, 2003.Google Scholar
Mitrushina, M, Boone, KB, Razani, J, D’elia, LF. Handbook of normative data for neuropsychological assessment, 2nd edition. New York: Oxford University Press, 2005.Google Scholar
Ivnik, RJ, Malec, JE, Tangalos, EG, Peterson, RC, Kokmen, E, Kurland, LT. Mayo’s older American’s normative studies: Updated AVLT norms for ages 56 to 97. Clin Neuropsychologist 1992; 6: 83104.Google Scholar
Schmidt, M. Rey auditory verbal learning test: A handbook. Los Angeles, CA: Western Psychological Services, 1996.Google Scholar
Reitan, R, Wolfson, D. Neuropsychological evaluation of older children. Tucson, AZ: Neuropsychology Press, 1992.Google Scholar
Wechsler, D. Wechsler Adult Intelligence Scale – Fourth edition (WAIS-IV). San Antonio, TX: Harcourt Assessment, 2008.Google Scholar
Varni, J, Seid, M, Rode, C. The PedsQL: Measurement model for the pediatric quality of life inventory. Med Care 1999; 37: 126139.Google Scholar
Varni, J, Seid, M, Kurtin, P. PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care 2001; 39: 800812.Google Scholar
Varni, J, Burwinkle, T, Seid, M, Skarr, D. The PedsQL 4.0 as a pediatric population health measure: Feasibility, reliability, and validity. Ambul Pediatr 2003; 3: 329341.2.0.CO;2>CrossRefGoogle ScholarPubMed
Diener, E, Emmons, RA, Larsen, RJ, Griffin, S. The Satisfaction With Life Scale. J Pers Assess 1985; 49: 7175.Google Scholar
Ayr, L, Yeates, K, Taylor, H, Brown, M. Dimensions of post-concussive symptoms in children with mild traumatic brain injuries. J Int Neuropsychol Soc 2009; 15: 1930.Google Scholar
King, NS, Crawford, S, Wenden, FJ, Moss, NE, Wade, DT. The Rivermead Post Concussion Symptoms Questionnaire: A measure of symptoms commonly experienced after head injury and its reliability. J Neurol 1995; 242: 587592.Google Scholar
Derogatis, LR, Melisaratos, N. The Brief Symptom Inventory: An introductory report. Psychol Med 1983; 13: 595605.Google Scholar
Achenbach, T. Manual for child behavior checklist/4–18 and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry, 1991.Google Scholar
Wagner, R, Torgesen, J, Rashotte, C. Comprehensive test of phonological processing. Examiner’s manual. San Antonio, TX: Pearson Assessments, 1999.Google Scholar
Wiederholt, J, Bryant, B. Gray Oral Reading Test(GORT-4) manual, fourth edition. San Antonio, TX: Pearson Assessments, 2001.Google Scholar
Connelly, J. KeyMath 3 diagnostic assessment. San Antonio, TX: Pearson Education, 2007.Google Scholar
Torgesen, J, Wagner, R, Rashotte, C. Test of word reading efficiency. Austin, TX: Pro-Ed, 1999.Google Scholar
Woodcock, R, Mcgrew, K, Mather, N. Woodcock-Johnson tests of achievement manual, 3rd edition. Itasca, IL: Riverside Publishing, 2001.Google Scholar
Harrison, P, Oakland, T. Adaptive behavior assessment system, second edition. San Antonio, TX: Harcourt Assessment, 2003.Google Scholar
Msall, ME, DiGaudio, K, Rogers, BT, LaForest, S, Catanzaro, NL, Campbell, J, et al. The Functional Independence Measure for Children (WeeFIM): conceptual basis and pilot use in children with developmental disabilities. Clin Pediatr 1994; 33: 421430.Google Scholar
Malec, JF, Lezak, MD. Manual for the Mayo-Portland Adaptability Inventory (MPAI-4) for adults, children and adolescents; revised with adaptations for pediatric version added January 2008. Available at: http://tbims.org/combi/mpai/manual.pdf.Google Scholar
Granger, C. The emerging science of functional assessment: Our tool for outcomes analysis. Arch Phys Med Rehabil 1998; 79: 235240.Google Scholar
Bedell, G. Developing a follow-up survey focused on participation of children and youth with acquired brain injuries after inpatient rehabilitation. NeuroRehabilitation 2004; 19: 191205.Google Scholar
Bedell, G, Dumas, H. Social participation of children and youth with acquired brain injuries discharged from inpatient rehabilitation: A follow-up study. Brain Inj 2004; 18: 6582.CrossRefGoogle ScholarPubMed
Prinz, R, Foster, S, Kent, R, Kd, OL. Multivariate assessment of conflict in distressed and nondistressed parent-adolescent dyads. J Appl Behav Anal 1979; 12: 691700.Google Scholar
Robin, A, Foster, S. Negotiating parent adolescent conflict: A behavioral family systems approach. New York: Guilford, 1989.Google Scholar
Burgess, ES, Drotar, D, Taylor, HG, Wade, S, Stancin, T, Yeates, KO. The family burden of injury interview: Reliability and validity studies. J Head Trauma Rehabil 1999; 14: 394405.Google Scholar
Andreasen, NC, Endicott, J, Spitzer, RL, Winokur, G. The family history method using diagnostic criteria. Reliability and validity. Arch Gen Psychiatry 1977; 34: 12291235.Google Scholar
Epstein, N, Baldwin, L, Bishop, D. The McMaster family assessment device. J Marital Fam Ther 1983; 9: 171180.Google Scholar
Beers, S, Hahner, T, Adelson, P. Validity of a pediatric version of the Glasgow Outcome Scale – Extended (GOS-E Peds). J Neurotrauma 2005; 22: 1224.Google Scholar
Hotz, G, Helm-Estabrooks, N, Nelson, NW, Plante, E. Pediatric Test of Brain Injury (PTBI). Baltimore, MD: Paul H. Brookes Publishing, 2010.Google Scholar
Jennett, B, Bond, M. Assessment of outcome after severe brain damage. Lancet 1975; 1: 480484.Google Scholar
Teasdale, GM, Pettigrew, LE, Wilson, JT, Murray, G, Jennett, B. Analyzing outcome of treatment of severe head injury: A review and update on advancing the use of the Glasgow Outcome Scale. J Neurotrauma 1998; 15: 587597.Google Scholar
Mackenzie, EJ, McCarthy, ML, Ditunno, JF, Forrester-Staz, C, Gruen, GS, Marion, DW, et al. Using the SF-36 for characterizing outcome after multiple trauma involving head injury. J Trauma 2002; 52: 527534.Google Scholar
EuroQol Group. EuroQol – a new facility for the measurement of health-related quality of life. Health Policy 1990; 16: 199208.Google Scholar
Bayley, N. Bayley scales of infant and toddler development, third edition. San Antonio, TX: Psychological Corporation, 2005.Google Scholar
Briggs-Gowan, M, Carter, A. Brief Infant Toddler Social Emotional Assessment (BITSEA). San Antonio, TX: Pearson Education, 2006.Google Scholar
Mullen, E. Mullen scales of early learning. Circle Pines, MN: American Guidance Service, 1995.Google Scholar
Espy, K. The shape school: Assessing executive function in preschool children. Dev Neuropsychol 1997; 13: 495499.Google Scholar
Espy, K, Cwik, M. The development of a Trail Making Test in young children: The TRAILS-P. Clin Neuropsychol 2004; 18: 112.Google Scholar
Coplan, J, Gleason, J. Unclear speech: Recognition and significance of unintelligible speech in preschool children. Pediatrics 1988; 82: 447452.Google Scholar
Campbell, T. Functional treatment outcomes for young children with neurogenic communication disorders. Semin Speech Lang 1999; 19: 223247.Google Scholar
Semel, W, Wiig, E, Secord, W. Clinical evaluation of language fundamentals, fourth edition. San Antonio, TX: Pearson Assessments, 2003.Google Scholar
Carrow-Woolfolk, E. Comprehensive assessment of spoken language. Circle Pines, MN: American Guidance Service, 1999.Google Scholar
Goldman, R, Fristoe, M. Goldman-Fristoe test of articulation, second edition. San Antonio, TX: Pearson Assessments, 2000.Google Scholar
Miller, J, Chapman, J. The SALT guide. Standard version, 8th edition. Madison, WI: Language Analysis Laboratory, Waisman Center, University of Wisconsin, 2004.Google Scholar
Dunn, L, Dunn, D. Peabody picture vocabulary test. Examiner’s manual, fourth edition. San Antonio, TX: Pearson Assessments, 2007.Google Scholar
Dunn, L, Lugo, D, Padilla, E, Dunn, L. Test de vocabulario en imágenes Peabody. San Antonio, TX: Pearson Assessments, 1986.Google Scholar
Shriberg, L, Austin, D, Lewis, B, McSweeney, J, Wilson, D. The percentage of consonants correct (PCC) metric. Extension and reliability data. J Speech Lang Hear Res 1997; 40: 708722.Google Scholar
Wiig, E, Secord, W. Test of language competence, expanded edition. San Antonio, TX: Psychological Corporation, 1989.Google Scholar
Hayden, D, Square, P. Verbal Motor Assessment of Children (VMPAC). San Antonio, TX: Pearson, 1999.Google Scholar
Nelson, HE. The National Adult Reading Test (NART): Test manual. Windsor, UK: NFER-Nelson, 1982.Google Scholar
Keane, TM, Fairbank, JA, Caddell, JM, Zimering, RT, Taylor, KL, Mora, C. Clinical evaluation of a measure to assess combat exposure. Psychol Assess 1989; 1: 5355.Google Scholar
French, L, McCrea, M, Baggett, M. The Military Acute Concussion Evaluation (MACE). J Special Ops Med 2008; 8: 6877.Google Scholar
Beery, K, Buktenica, N, Beery, N. Beery-Buktenica developmental test of visual-motor integration, sixth edition. San Antonio, TX: Pearson Assessments, 2010.Google Scholar
Gioia, G, Espy, K, Isquith, P. Behavior rating inventory of executive functionPreschool version. Odessa, FL: Psychological Assessment Resources, 2003.Google Scholar
Gioia, G, Isquith, P, Guy, S, Kenworthy, L. BRIEF: Behavior Rating Inventory of Executive Function. Lutz, FL: Psychological Assessment Resources, 2000.Google Scholar
Guy, S, Isquith, P, Gioia, G. Behavior Rating Inventory of Executive FunctionSelf report version. Odessa, FL: Psychological Assessment Resources, 2004.Google Scholar
Conners, C. Continuous performance test. Technical guide and software manual, second edition. North Tonawanda, NY: MultiHealth Systems, 2004.Google Scholar
Taylor, H, Schatsneider, C, Rich, D. Sequelae of Haemophilus influenzae meningitis: Implications for the study of brain disease and development. In: Tramontana, M, Hooper, S, editors. Advances in clinical neuropsychology. I. New York: Springer-Verlag, 1992, pp. 50108.Google Scholar
Delis, D, Kaplan, E, Kramar, J. Delis-Kaplan executive function system. San Antonio, TX: Pearson Assessment, 2001.Google Scholar
Eriksen, B, Eriksen, C. Effects of noise letters upon identification of a target letter in a nonsearch task. Percept Psychophys 1974; 16: 143149.CrossRefGoogle Scholar
Macdonald, S. Assessment of higher level cognitive-communication functions in adolescents with ABI: Standardization of the student version of the functional assessment of verbal reasoning and executive strategies (S-FAVRES). Brain Inj 2016; 30: 295310.Google Scholar
Manly, T, Robertson, I, Anderson, V, Nimmo-Smith, I. TEA-Ch: The Test of Everyday Attention for Children. Bury St. Edmunds, England: Thames Valley Test Company, 1999.Google Scholar
Isquith, P, Roth, R, Gioia, G. Tasks of Executive Control (TEC). Odessa, FL: Psychological Assessment Resources, 2010.Google Scholar
Reynolds, CR, Voress, JK. Test of memory and learning – Second edition. Austin, TX: PRO-ED, 2007.Google Scholar
Gamino, JF, Chapman, SB, Cook, LG. Strategic learning in youth with traumatic brain injury: Evidence for stall in higher-order cognition. Top Lang Disorders 2009; 29: 224235.Google Scholar
Sheslow, D, Adams, W. Wide Range Assessment of Memory and Learning; second edition (WRAML2). Lutz, FL: Psychological Assessment Resources, 2003.Google Scholar
Mathews, C, Kløve, K. Instruction manual for the adult neuropsychology test battery. Madison, WI: University of Wisconsin Medical School, 1964.Google Scholar
Smith, A. Symbol Digit Modalities Test (SDMT). Torrance, CA: Western Psychological Services, 1973.Google Scholar
Reeves, D, Kane, R, Winter, K. Automated Neuropsychological Assessment Metrics (ANAM): Test administrator’s guide version 3.11 (report no. NCRF-95-01). San Diego, CA: National Cognitive Recovery Foundation, 1995.Google Scholar
Miller, D, Nowinski, C, Victorson, D, Peterman, A, Perez, L. The Neuro-QOL project: Establishing research priorities through qualitative research and consensus development. Qual Life Res 2005; 14: 2031.Google Scholar
Perez, L, Huang, J, Jansky, L, Nowinski, C, Victorson, D, Peterman, A, et al. Using focus groups to inform the Neuro-QOL measurement tool: Exploring patient-centered, health-related quality of life concepts across neurological conditions. J Neurosci Nurs 2007; 39: 342353.Google Scholar
Cella, D, Yount, S, Rothrock, N, Gershon, R, Cook, K, Reeve, B, Ader, D, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH Roadmap Cooperative Group during its first two years. Med Care 2007; 45(5: Suppl. 1): S3S11.CrossRefGoogle ScholarPubMed
Bruininks, RH, Bruininks, BD. BOT-2, Bruininks-Oseretsky Test of Motor Proficiency, second edition. Minneapolis, MN: Pearson Assessments, 2005.Google Scholar
Russell, D, Rosenbaum, P, Cadman, D, Gowland, C, Hardy, S, Jarvis, S. The Gross Motor Function Measure: A means to evaluate the effects of physical therapy. Dev Med Child Neurol 1989; 31: 341352.Google Scholar
Russell, D, Avery, L, Rosenbaum, P, Raina, P, Walter, S, Palisano, R. Improved scaling of the Gross Motor Function Measure for children with cerebral palsy: Evidence of reliability and validity. Phys Ther 2000; 80: 873885.Google Scholar
Hays, RD, Spritzer, KL, Amtmann, D, Lai, J-S, DeWitt, EM, Rothrock, N, et al. Upper-extremity and mobility subdomains from the Patient-Reported Outcomes Measurement Information System (PROMIS) Adult Physical Functioning Item Bank. Arch Phys Med Rehabil 2013; 94: 22912296.Google Scholar
Folio, MR, Fewell, RR. Peabody Developmental Motor Scales, second edition (PDMS-2). San Antonio, TX: Pearson, 2000.Google Scholar
Iverson, GL, Kaarto, ML, Koehle, MS. Normative data for the balance error scoring system: Implications for brain injury evaluations. Brain Inj 2008; 22: 147152.CrossRefGoogle ScholarPubMed
Wilde, EA, Mccauley, SR, Kelly, TM, Levin, HS, Pedroza, C, Clifton, GL, et al. Feasibility of the Neurological Outcome Scale for Traumatic Brain Injury (NOS-TBI) in adults. J Neurotrauma 2010; 27: 975981.Google Scholar
Gioia, G, Schneider, J, Vaughan, C, Isquith, P. Which symptom assessments and approaches are uniquely appropriate for paediatric concussion? Br J Sports Med 2009; 43: i13–i22.Google Scholar
Meterko, M, Baker, E, Stolzmann, KL, Hendricks, AM, Cicerone, KD, Lew, HL. Psychometric assessment of the Neurobehavioral Symptom Inventory-22: The structure of persistent postconcussive symptoms following deployment-related mild traumatic brain injury among veterans. J Head Trauma Rehabil 2012; 27: 5562.Google Scholar
Gerson, A, Gerring, J, Freund, L, Joshi, P, Capozzoli, J, Brady, K, et al. The Children’s Affective Lability Scale: A psychometric evaluation of reliability. Psychiatry Res 1996; 65: 189198.CrossRefGoogle Scholar
Gerring, J, Freund, L, Gerson, A, Joshi, P, Capozzoli, J, Frosch, E, et al. Psychometric characteristics of the Children’s Motivation Scale. Psychiatry Res 1996; 63: 205217.Google Scholar
Kay, S, Wolkenfeld, F, Murrill, L. Profiles of aggression among psychiatric patients. I. Nature and prevalence. J Nerv Ment Dis 1988; 176: 539546.Google Scholar
Max, JE, Castillo, CS, Lindgren, SD, Arndt, S. The Neuropsychiatric Rating Schedule: Reliability and validity. J Am Acad Child Adol Psychiatry 1998; 37: 297304.Google Scholar
Kaufman, J, Birmaher, B, Brent, D, Rao, U, Flynn, C, Williamson, D, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime version (K-SADS-PL): Initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 1997; 36: 980988.Google Scholar
Monga, S, Birmaher, B, Chiappetta, L, Brent, D, Kaufman, J, Bridge, J, et al. Screen for Child Anxiety-Related Emotional Disorders (SCARED): Convergent and divergent validity. Depress Anxiety 2000; 12: 8591.Google Scholar
Hale, WR, Raaijmakers, Q, Muris, P, Meeus, W. Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED) in the general adolescent population. J Am Acad Child Adolesc Psychiatry 2005; 44: 283290.Google Scholar
Birmaher, B, Brent, D, Chiappetta, L, Bridge, J, Monga, S, Baugher, M. Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): A replication study. J Am Acad Child Adolesc Psychiatry 1999; 38: 12301236.Google Scholar
Birmaher, B, Khetarpal, S, Brent, D, Cully, M, Balach, L, Kaufman, J, et al. The Screen for Child Anxiety Related Emotional Disorders (SCARED): Scale construction and psychometric characteristics J Am Acad Child Adolesc Psychiatry 1997; 36: 545553.Google Scholar
Angold, A, Costello, E, Messer, S, Pickles, A, Winder, F, Silver, D. Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. Int J Methods Psychiatr Res 1995; 5: 237249.Google Scholar
Costello, E, Angold, A. Scales to assess child and adolescent depression: Checklists, screens and nets. J Am Acad Child Adolesc Psychiatry 1988; 27: 726737.Google Scholar
Goodman, R. The Strengths and Difficulties Questionnaire: A research note. J Child Psychol Psychiatry 1997; 43: 11591167.Google Scholar
Steinberg, AM, Brymer, MJ, Decker, KB, Pynoos, RS. The University of California at Los Angeles Post-traumatic Stress Disorder Reaction Index. Curr Psychiatry Rep 2004; 6: 96100.Google Scholar
WHO ASSIST Working Group. The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST): Development, reliability and feasibility. Addiction 2002; 97: 11831194.Google Scholar
Saunders, JB, Aasland, OG, Babor, TF, De La Fuente, JR, Grant, M. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption – II. Addiction 1993; 88: 791804.Google Scholar
Beck, AT, Steer, RA, Ball, R, Ranieri, W. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67: 588597.Google Scholar
Radloff, LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Measure 1977; 1: 385401.Google Scholar
Weathers, FW, Blake, DD, Schnurr, PP, Kaloupek, DG, Marx, BP, Keane, TM. The Clinician-Administered PTSD scale for DSM-5 (CAPS-5). 2013. Available at www.ptsd.va.gov.Google Scholar
Tellegen, A, Ben-Porath, YS. Minnesota Multiphasic Personality Inventory technical manual. Minneapolis, MN: University of Minneapolis Press, 2008.Google Scholar
Kroenke, K, Spitzer, RL, Williams, JB. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 2001; 16: 606613.CrossRefGoogle ScholarPubMed
Weathers, FW, Litz, BT, Keane, TM, Palmieri, PA, Marx, BP, Schnurr, PP. The PTSD Checklist for DSM-5 (PCL-5). 2013. Available at www.ptsd.va.gov.Google Scholar
Corrigan, JD, Bogner, J, Lamb-Hart, G, Sivak-Sears, N. Technical report on problematic substance use identified in the TBI Model Systems National Dataset. Center for Outcome Measurement in Brain Injury. Available at: www.tbims.org/combi/subst/index.Google Scholar
Ewing-Cobbs, L, Levin, H, Fletcher, J, Miner, M, Eisenberg, H. The children’s orientation and amnesia test: Relationship to severity of acute head injury and to recovery of memory. Neurosurg. 1990; 27: 683691.Google Scholar
Levin, HS, O'Donnell, VM, Grossman, RG. The Galveston Orientation and Amnesia Test. A practical scale to assess cognition after head injury. J Nerv Mental Dis 1979; 167: 675684.Google Scholar
Giacino, JT, Kalmar, K, Whyte, J. The JFK Coma Recovery Scale-Revised: Measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004; 85: 20202029.Google Scholar
Yeates, K, Schultz, L, Selman, R. Bridging the gaps in child-clinical assessment: Toward the application of social-cognitive development theory. Clin Psychol Rev 1990; 10: 567588.Google Scholar
Baron-Cohen, S, Wheelwright, S, Scahill, V, Lawson, J, Spong, A. Are intuitive physics and intuitive psychology independent? A test with children with Asperger syndrome. J Dev Learn Disord 2001; 5: 4778.Google Scholar
Turkstra, L. Conversation-based assessment of social cognition in adults with traumatic brain injury. Brain Inj 2008; 22: 397409.Google Scholar
Bedell, G. Further validation of the Child and Adolescent Scale of Participation (CASP). Dev Neurorehabil 2009; 12: 342351.Google Scholar
Ziviani, J, Desha, L, Feeney, R, Boyd, R. Measures of participation outcomes and environmental considerations for children with acquired brain injury: A systematic review. Brain Impair 2010; 11: 93112.Google Scholar
Haley, S, Coster, W, Ludlow, L, Haltiwanger, J, Andrellos, P. Pediatric evaluation of disability inventory: Development, standardization, and administration manual, version 1.0. Boston, MA: Trustees of Boston University, Health and Disability Research Institute, 1992.Google Scholar
Elliott, S, Gresham, F, Freeman, T, Mccloskey, G. Teacher and observer ratings of children’s social skills: Validation of the Social Skills Rating Scale. J Psychoeduc Assess 1988; 6: 152161.Google Scholar
Whiteneck, GG, Brooks, CA, Charlifue, S, Gerhart, KA, Mellick, M, Overholser, D, et al. Guide for use of the chart: Craig Handicap Assessment and reporting technique. Englewood, CO: Craig Hospital, 1988, 1992.Google Scholar
Bogner, J, Bellon, K, Kolakowsky-Hayner, SA, Whiteneck, G. Participation Assessment With Recombined Tools–Objective (PART-O). J Head Trauma Rehabil 2013; 28: 337339.Google Scholar
Collie, A, Darby, D, Maruff, P. Computerised cognitive assessment of athletes with sports related head injury. Br J Sports Med 2001; 35: 297302.Google Scholar
Gualtieri, CT, Johnson, LG. Reliability and validity of a computerized neurocognitive test battery, CNS vital signs. Arch Clin Neuropsychol 2006; 21: 623643.Google Scholar
Erlanger, DM, Feldman, D, Kutner, KC. Concussion Resolution Index. New York, NY: HeadMinder, 1999.Google Scholar
Keith, RW. Development and standardization of SCAN-C test for auditory processing disorders in children. J Am Acad Audiol 2000; 11: 438445.Google Scholar
Keith, RW. Development and standardization of SCAN-A: Test of auditory processing disorders in adolescents and adults. J Am Acad Audiol 1995; 6: 286292.Google Scholar
Arnott, W, Goli, T, Bradley, A, Smith, A, Wilson, W. The filtered words test and the influence of lexicality. J Speech Lang Hear Res 2014; 57: 17221730.Google Scholar
O’Beirne, GA, Mcgaffin, AJ, Rickard, NA. Development of an adaptive low-pass filtered speech test for the identification of auditory processing disorders. Int J Pediatr Otorhinolaryngol 2012; 76: 777782.Google Scholar
Auditec. Quality auditory test recordings since 1972. https://auditecincorporated.wordpress.com/.Google Scholar
Keith, RW. Random gap detection test [CD; CD-Rom]. Bloomington, MN: NCS Pearson.Google Scholar
Keith, RW. Standardization of the time compressed sentence test. J Educ Audiol 2002; 10: 1520.Google Scholar
Keith, RW. Time compressed sentence test, Examiner’s manual. St. Louis, MO: Auditec, 2002.Google Scholar
Jacobson, GP, Newman, CW. The development of the Dizziness Handicap Inventory. Arch Otolaryngol Head Neck Surg 1990; 116: 424427.Google Scholar
Newman, CW, Weinstein, BE, Jacobson, GP, Hug, GA. The hearing handicap inventory for adults: Psychometric adequacy and audiometric correlates. Ear Hear 1990; 11: 430433.Google Scholar
Ventry, IM, Weinstein, BE. The hearing handicap inventory for the elderly: A new tool. Ear Hear 1982; 3: 128134.Google Scholar
Meikle, MB, Henry, JA, Griest, SE, Stewart, BJ, Abrams, HB, Mcardle, R, et al. The tinnitus functional index: Development of a new clinical measure for chronic, intrusive tinnitus. Ear Hear 2012; 33: 153176.Google Scholar
Fackrell, K, Hall, DA, Barry, J, Hoare, DJ. Tools for tinnitus measurement: development and validity of questionnaires to assess handicap and treatment effects. In: Signorelli, F, Turjman, F, editors. Tinnitus: Causes, Treatment and Short and Long-term Health Effects (pp. 1360). New York: Nova Science Publishers, 2014.Google Scholar
Chandra, N. New Zealand validation of the Tinnitus Functional Index. 2013. Available at www.fmhs.auckland.ac.nz/assets/fmhs/soph/bhsc_hons/docs/presentation-slides/2013/navshika-chandra.pdf.Google Scholar
Newman, CW, Jacobson, GP, Spitzer, JB. Development of the tinnitus handicap inventory. Arch Otolaryngol Head Neck Surg 1996; 122: 143148.Google Scholar
Jacobson, BH, Johnson, A, Grywalski, C, Silbergleit, A, Jacobson, G, Benninger, MS, et al. The voice handicap index (VHI) development and validation. Am J Speech-Lang Pathol 1997; 6: 6670.Google Scholar
Rosen, CA, Lee, AS, Osborne, J, Zullo, T, Murry, T. Development and validation of the voice handicap index-10. Laryngoscope 2004; 114: 15491556.Google Scholar
NINDS Common Data Elements. Sport-related concussion. 2018. Available at www.commondataelements.ninds.nih.gov/SRC.aspx#tab=Data_Standards.Google Scholar
Fitbir. Informatics System. 2015. https://fitbir.nih.gov/.Google Scholar
Clinical Data Interchange Standards Consortium. Traumatic brain injury therapeutic area. 2016. www.cdisc.org/traumatic-brain-injury-therapeutic-area.Google Scholar
U.S. Food and Drug Administration. FDA study data technical conformance guide; Technical specifications document, 2017. Available at www.fda.gov/downloads/ForIndustry/DataStandards/StudyDataStandards/UCM384744.pdf.Google Scholar
Yue, JK, Vassar, MJ, Lingsma, HF, Cooper, SR, Okonkwo, DO, Valadka, AB, et al. Transforming research and clinical knowledge in traumatic brain injury pilot: Multicenter implementation of the common data elements for traumatic brain injury. J Neurotrauma 2013; 30: 18311844.Google Scholar
Gershon, RC, Cella, D, Fox, NA, Havlik, RJ, Hendrie, HC, Wagster, MV. Assessment of neurological and behavioural function: The NIH Toolbox. Lancet Neurol 2010; 9: 138139.Google Scholar

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