Evidence available for patient-identified priorities in depression research: results of 11 rapid responses
Bibliographic record
Abstract
OBJECTIVES: Patient priority setting projects (PPSPs) can reduce research agenda bias. A key element of PPSPs is a review of available literature to determine if the proposed research priorities have been addressed, identify research gaps, recognise opportunities for knowledge translation (KT) and avoid duplication of research efforts. We conducted rapid responses for 11 patient-identified priorities in depression to provide a map of the existing evidence. DESIGN: Eleven rapid responses. DATA SOURCES: Single electronic database (PubMed). ELIGIBILITY CRITERIA: Each rapid response had unique eligibility criteria. For study designs, we used a stepwise inclusion process that started with systematic reviews (SRs) if available, then randomised controlled trials and observational studies as necessary. RESULTS: For all but one of the rapid responses we identified existing SRs (median 7 SRs per rapid response, range 0-179). There were questions where extensive evidence exists (ie, hundreds of primary studies), yet uncertainties remain. For example, there is evidence supporting the effectiveness of many non-pharmacological interventions (including psychological interventions and exercise) to reduce depressive symptoms. However, targeted research is needed that addresses comparative effectiveness of promising interventions, specific populations of interest (eg, children, minority groups) and adverse effects. CONCLUSIONS: We identified an extensive body of evidence addressing patient priorities in depression and mapped the results and limitations of existing evidence, areas of uncertainty and general directions for future research. This work can serve as a solid foundation to guide future research in depression and KT activities. Integrated knowledge syntheses bring value to the PPSP process; however, the role of knowledge synthesis in PPSPs and methodological approaches are not well defined at present.
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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 | 0.300 | 0.598 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".