S203. COMPENSATORY COGNITIVE APPROACHES TO IMPROVING FUNCTIONING IN PSYCHOSIS: SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Cognitive impairments in domains such as attention, memory, processing speed and executive functions are a central feature of psychotic disorders that have significant negative consequences for daily functioning, including activities of daily living, social and vocational roles. Compensatory approaches aim to minimise the impact of cognitive impairment on daily functioning through the use of aids or strategies to reduce cognitive load, in much the same way as glasses reduce the impact of vision impairment. The primary treatment target is real world community functioning and functional capacity, rather than cognition. There is now a need to synthesise the available evidence in this field so that treatment recommendations and future research directions can be better informed. A large body of research into compensatory approaches to cognition in psychosis exists, but this has never been comprehensively synthesised. The aim of this systematic review and meta-analysis is to examine the effects of compensatory approaches for cognitive deficits in psychotic disorders on i) functional outcomes and ii) other outcomes such as symptoms and quality of life. A systematic review and meta-analysis was conducted according to PRISMA guidelines. PsycINFO and MEDLINE electronic databases were searched from inception to October 2017 using multiple terms for ‘psychosis’, ‘cognition’ and ‘compensatory’. All papers retrieved from this search were double-screened and final inclusion/exclusion was determine by consensus. Data were double-extracted and risk of bias rated by two independent authors. Meta-analysis only included randomised-controlled trials. Standardised Mean Differences (SMD) were calculated to produce a single summary estimate using the random-effects model with 95% Confidence Intervals using Comprehensive Meta-Analysis (CMA) software. When means or standard deviations were not reported in the original articles, SMDs were calculated from data provided by the study authors. 2192 articles were identified via electronic and manual searches. Forty-two papers describing 40 independent studies were included in the review: case studies (n=4), case series (n=2), uncontrolled single arm pilot studies (n=5), within-subjects designs (n=1), quasi-randomised trials (n=2), and randomised controlled trials (n=26). The types of compensatory interventions included environmental adaptation and supports, internal and external self-management strategies, and errorless learning. Compensatory interventions were associated with improvements in global functioning post intervention (N=1,449; SMD=0.506; 95%CI=0.347, 0.665; p<.001). Improvements in global symptoms (N=849; SMD=-0.297; 95%CI=-0.484, -0.111; p=.002) and positive symptoms (N=784; SMD=-0.227; 95%CI=-0.416, -0.038; p=.018) were also found. Compensatory interventions were not associated with improvements in negative symptoms (N=736; SMD=-0.162; 95%CI=-0.382, 0.058; p=.150). The heterogeneity of findings was low. Compensatory approaches are effective for improving functioning in psychosis, with a medium effect size. General symptoms and positive symptoms appear to benefit from compensatory approaches, but compensatory approaches are not effective for improving negative symptoms. Future analyses will examine the durability of effects, effects of study quality and moderating factors such as pure vs. partially compensatory, treatment intensity/length, mode of delivery (group vs. individual), baseline functioning level and age of participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".