DEVELOPMENT AND VALIDATION OF A PREDICTION ALGORITHM FOR USE BY HEALTH PROFESSIONALS IN PREDICTION OF RECURRENCE OF MAJOR DEPRESSION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There exists very little evidence to guide clinical management for preventing recurrence of major depression. The objective of this study was to develop and validate a prediction algorithm for recurrence of major depression. METHODS: Wave 1 and wave 2 longitudinal data from the U.S. National Epidemiological Survey on Alcohol and Related Condition (2001/2002–2003/2004) were used. Participants with a major depressive episode at baseline and who had visited health professionals for depression were included in this analysis (n = 2,711). Mental disorders were assessed based on the DSM-IV criteria. RESULTS: With the development data (n = 1,518), a prediction model with 19 unique factors had a C statistics of 0.7504 and excellent calibration (P = .23). The model had a C statistics of 0.7195 in external validation data (n = 1,195) and 0.7365 in combined data. The algorithm calibrated very well in validation data. In the combined data, the 3-year observed and predicted risk of recurrence was 25.40% (95% CI: 23.76%, 27.04%) and 25.34% (95% CI: 24.73%, 25.95%), respectively. The predicted risk in the 1st and 10th decile risk group was 5.68% and 60.21%, respectively. CONCLUSIONS: The developed prediction model for recurrence of major depression has acceptable discrimination and excellent calibration, and is feasible to be used by physicians. The prognostic model may assist physicians and patients in quantifying the probability of recurrence so that physicians can develop specific treatment plans for those who are at high risk of recurrence, leading to personalized treatment and better use of resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it