Decision curve analysis as a framework to estimate the potential value of screening or other decision‐making aids
Bibliographic record
Abstract
OBJECTIVES: There is an increasing debate about the impact of mental health screening. We illustrate the use of a decision making framework that can be applied when there is no sufficient data to support a traditional cost-benefit analysis. METHODS: We conducted secondary analyses of data from 459 male prisoners who were screened upon intake. We compared the potential benefit of different approaches (screening, history taking, and universal interventions) to allocating treatment resources using decision curve analysis. RESULTS: Screening prisoners for distress at typical levels of sensitivity (75%) and specificity (71%) were estimated to provide the greatest net benefit if between 2 and 5 false positives per detected illness are tolerable. History taking and self-harm screening provide the largest net benefit when only 1 or 2 false positives per detected illness would be tolerable. The benefits of screening were less among those without a recent psychiatric history, ethnic minorities, and those with fewer psychosocial needs. CONCLUSIONS: Although screening has potential to increase detection of treatment, important subgroup differences exist. Greater consideration of responses to positive screens or alternatives to screening are needed to maximize the impact of efforts to improve detection and treatment of mental illness.
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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 | 0.188 | 0.349 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".