The Great Black-backed Gull Larus marinus is a generalist species that inhabits temperate and arctic coasts of the north Atlantic Ocean. In recent years, there has been growing concern about population declines at local and regional scales; however, there has been no attempt to robustly assess Great Black-backed Gull population trends across its global range. We obtained the most recent population counts across the species’ range and analysed population trends at a global, continental, and national scale over the most recent three-generation period (1985–2021) following IUCN Red List criteria. We found that, globally, the species has declined by 43%–48% over this period (1.2–1.3% per annum, respectively), from an estimated 291,000 breeding pairs to 152,000–165,000 breeding pairs under two different scenarios. North American populations declined more steeply than European ones (68% and 28%, respectively). We recommend that Great Black-backed Gull should be uplisted from ‘Least Concern’ to ‘Vulnerable’ on the IUCN Red List of Threatened Species under criterion A2 (an estimated reduction in population size >30% over three generations).

Background: Idiopathic Normal Pressure Hydrocephalus (iNPH) is a disorder of the elderly with progressive worsening of gait and balance, cognition, and urinary control which requires assessment using criteria recommended by International iNPH guidelines. Methods: Adult Hydrocephalus Clinical Research Network (AHCRN) prospective registry data from 5-centers over a 50-month interval included entry criteria; demographics; comorbidities; examination findings using standard AHCRN gait and neuropsychology assessments; shunt procedures, complications of CSF drainage, complications within 30 days of surgery, and 1-year postoperative follow-up. Results: 547 patients were referred for assessment of suspected-iNPH. 123 patients(21.6%) did not meet clinical criteria to proceed with further testing. 424 patients(74.4%;mean age 76.7 ± 6.0 years;males=269) underwent an LP or lumbar drain, and 193(45.6%) underwent insertion of a ventriculoperitoneal shunt. By 8-12 months after shunt surgery, gait velocity was 0.96±0.35m/s (54% faster than pre-CSF-drainage). Mean MoCA scores increased from 21.0 ± 5.0(median=22.0) at baseline to 22.6±5.5(median=24) 12-months post-surgery. Gait and cognitive improvements were clinically significant. No deaths occurred. 8% of shunt-surgery patients experienced minor complications. The 30-day reoperation rate was 4.1%. Conclusions: This AHCRN study demonstrated that CSF-drainage testing of patients with suspected-iNPH successfully identified those who could undergo CSF-shunt surgery with a high rate of improvement and a low rate of complications.

Background: To describe preliminary results of a multi-center, randomized, blinded, placebo-controlled, pilot trial of shunt surgery in idiopathic normal pressure hydrocephalus (iNPH). Methods: Five sites of the Adult Hydrocephalus Clinical Research Network (AHCRN) randomized 18 patients scheduled for ventriculoperitoneal shunting based on CSF-drainage response. Patients were randomized to a Codman® Certas® Plus valve with SiphonGuard at either setting 4 (Active, N=9) or setting 8/”virtual off” (Placebo, N=9). Patients and assessors were blinded to the shunt setting. Outcomes included 10-meter gait velocity, cognitive function, and bladder activity scores. The prespecified primary analysis compared changes in 4-month gait velocity in the Active versus Placebo groups. Placebo-set shunts were then blindly adjusted to the active setting and all patients underwent 8 and 12-month post-surgical assessment. Results: At 4-months, gait velocity increased by 0.28±0.28m/s in the Active Group and 0.04±0.17m/s in the Placebo Group (p=0.071). Overactive Bladder (OAB-q) scores significantly improved in the Active versus Placebo groups (p=0.007). At 8 months, Placebo gait velocity increased by 0.36±0.27m/s and was comparable to the Active Group (0.40±0.20m/s; p=0.56). Conclusions: This AHCRN study shows a trend suggesting gait velocity improves more at an Active shunt setting than a Placebo shunt setting and demonstrates the feasibility of a placebo-controlled trial in iNPH.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.