To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
There is preliminary evidence that CBT may be helpful for improving symptoms of misophonia, but the key mechanisms of change are not yet known for this disorder of decreased tolerance to everyday sounds. This detailed case study aimed to describe the delivery of intensive, formulation-driven CBT for an individual with misophonia and report on session-by-session outcomes using a multi-dimensional measurement tool (SFive). The patient was offered 12 hours of treatment over five sessions, using transdiagnostic and misophonia-specific interventions. Reliable and clinically significant change was found from baseline to one-month follow-up. Visual inspection of outcome graphs indicated that change occurred on the ‘outbursts’ and ‘internalising appraisals’ SFive subscales following assessment, and on the ‘emotional threat’ subscale after the first treatment session. The other two subscales started and remained below a clinically significant level. The biggest symptom change appeared to have occurred after the second session, which included interventions engaging with trigger sounds. The results demonstrated the individualised nature of misophonia, supporting the use of individually tailored treatment for misophonia and highlighting the importance of using a multi-dimensional measurement tool.
Key learning aims
(1) To understand misophonic distress from a CBT perspective.
(2) To learn a formulation-driven approach to misophonia.
(3) To apply transdiagnostic interventions to misophonia.
(4) To learn about misophonia-specific interventions.
(5) To consider the value of a multi-dimensional measure of misophonia.
There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD).
In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10–20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers—C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2)—measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response.
Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.
Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.
The UN Human Rights Committee's finding in Teitiota v New Zealand has garnered widespread global attention for its recognition that the effects of climate change may put people's lives at risk or expose them to cruel, inhuman or degrading treatment, thus triggering States’ non-refoulement obligations. However, a secondary—and highly problematic—consequence of the decision has been its confusing and misplaced focus on ‘imminence’ of harm. This reflects a concerning, albeit uneven, trend in human rights cases generally (and cases concerning climate change and human rights, in particular) to recognize violations only where rights are immediately threatened. This short article reflects on the assumptions that Teitiota has triggered about the place of imminence in international protection claims, identifies the source of confusion, and suggests a more appropriate framework to guide a category of case that is likely to become the subject of intense litigation in the future.
Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.
In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.
A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.
A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.
Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.
Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.
The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.
This article is an output of a major research project examining the notion of imminence in the law on international protection. It is the first piece of scholarship to identify an emerging trend, namely the introduction of imminence—whether invoked implicitly or explicitly—as a potential barrier to refugee status or complementary protection. The article analyses the jurisprudence of relevant international bodies and courts and critiques the validity of this notion as a tool for assessing States’ protection obligations.
Email your librarian or administrator to recommend adding this to your organisation's collection.