Additional file 1 of An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data
Bibliographic record
Abstract
Additional file 1: Table A1. Distribution of EPDS scores by cut-off among participants with depression and without depression. Table A2. Estimated sensitivity and specificity, 95% confidence intervals (CI) and CI widths for each cut-off when BREM [13] was fitted to the published and full IPD dataset. Table A3. Estimated sensitivity and specificity, 95% confidence intervals (CI) and CI widths for each cut-off when Steinhauser et al. [16] model was fitted to the published and full IPD dataset. Table A4. Estimated sensitivity and specificity, 95% confidence intervals (CI) and CI widths for each cut-off when Jones et al. [18] model is fit to the published (top) and full IPD (bottom) dataset. Table A5. Estimated sensitivity and specificity, 95% confidence intervals (CI) and CI widths for each cut-off when Hoyer and Kuss [19] model is fit to the published (top) and full IPD (bottom) dataset. Figure A1. Distribution of published EPDS cut-offs by the number of primary studies included in the meta-analyses using the published dataset. Figure A2. Distribution of EPDS scores among participants with depression (red) and without depression (blue). Purple portions are part of both the blue and red distributions. Figure A3. Estimated sensitivity (left) and specificity (right) and 95% Confidence Interval (Credible Interval for Jones et al. [18]) by cut-off for the BREM [13], Steinhauser et al. [16], Jones et al. [18] and Hoyer and Kuss [19] methods applied to the full IPD dataset. Figure A4. Estimated sensitivity (left) and specificity (right) and 95% Confidence Interval (Credible Interval for Jones et al. [18]) by cut-off for the BREM [13], Steinhauser et al. [16], Jones et al. [18] and Hoyer and Kuss [19] methods applied to the published dataset.
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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 | 0.021 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.817 | 0.084 |
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".