Screening and case-finding instruments for depression: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Screening and case-finding has been proposed as a simple, quick and cheap method to improve the quality of care for depression. We sought to establish the effectiveness of screening in improving the recognition of depression, the management of depression and the outcomes of patients with depression. METHODS: We performed a Cochrane systematic review of randomized controlled trials conducted in nonmental health settings that included case-finding or screening instruments for depression. We conducted a meta-analysis and explored heterogeneity using meta-regression techniques. RESULTS: Sixteen studies with 7576 patients met our inclusion criteria. We found that the use of screening or case-finding instruments were associated with a modest increase in the recognition of depression by clinicians (relative risk [RR] 1.27, 95% confidence interval [CI] 1.02 to 1.59). Questionnaires, when administered to all patients and the results given to clinicians irrespective of baseline score, had no impact on recognition (RR 1.03, 95% CI 0.85 to 1.24). Screening or case finding increased the use of any intervention by a relative risk of 1.30 (95% CI 0.97 to 1.76). There was no evidence of influence on the prescription of antidepressant medications (RR 1.20, 95% CI 0.87 to 1.66). Seven studies provided data on outcomes of depression, and no evidence of an effect was found (standardized mean difference -0.02, 95% CI -0.25 to 0.20). INTERPRETATION: If used alone, case-finding or screening questionnaires for depression appear to have little or no impact on the detection and management of depression by clinicians. Recommendations to adopt screening strategies using standardized questionnaires without organizational enhancements are not justified.
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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 | 0.038 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.070 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".