Negative Symptom Interventions in Youth at Risk of Psychosis: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Objective: Youth at clinical high risk (CHR) for psychosis often demonstrate significant negative symptoms, which have been reported to be predictive of conversion to psychosis and a reduced quality of life but treatment options for negative symptoms remain inadequate. Therefore, we conducted a systematic review and network meta-analysis of all intervention studies examining negative symptom outcomes in youth at CHR for psychosis. Method: The authors searched PsycINFO, Medline, Embase, CINAHL, and EBM from inception to December 2016. Studies were selected if they included any intervention that reported follow-up negative symptoms in youth at CHR for psychosis. Treatment comparisons were evaluated using both pairwise and network meta-analyses. Due to the differences in negative symptom scales the effect sizes were reported as the standardized mean difference (SMD). Results: Of 3027 citations, 32 studies met our inclusion criteria, including a total of 2463 CHR participants. The null hypothesis was not rejected for any of the 11 treatments. N-methyl-D-aspartate-receptor (NMDAR) modulators trended toward a significant reduction in negative symptoms compared to placebo (SMD = -0.54; 95% CI = -1.09 to 0.02; I2 = 0%, P = .06). In respective order of descending effectiveness as per the treatment hierarchy, NMDAR modulators were more effective than family therapy, need-based interventions, risperidone, amisulpride, cognitive behavioral therapy, omega-3, olanzapine, supportive therapy, and integrated psychological interventions. Conclusions: Efficacy and effectiveness were not confirmed for any negative symptom treatment. Many studies had small samples and the majority were not designed to target negative symptoms.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".