Sample sizes and precision of estimates of sensitivity and specificity from primary studies on the diagnostic accuracy of depression screening tools: a survey of recently published studies
Why this work is in the frame
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Bibliographic record
Abstract
Depression screening tools are useful to the extent that they accurately discriminate between depressed and non-depressed patients. Studies without enough patients to generate precise estimates make it difficult to evaluate accuracy. We conducted a survey of recently published studies on depression screening tool accuracy to evaluate the percentage with sample size calculations; the percentage that provided confidence intervals; and precision, based on the width and lower bounds of 95% confidence intervals for sensitivity and specificity. We calculated 95% confidence intervals, if possible, when not provided. Only three of 89 studies (3%) described a viable sample size calculation. Only 30 studies (34%) provided reasonably accurate confidence intervals. Of 86 studies where 95% confidence intervals were provided or could be calculated, only seven (8%) had interval widths for sensitivity of ≤ 10%, whereas 53 (62%) had widths of ≥ 21%. Lower bounds of confidence intervals were < 80% for 84% of studies for sensitivity and 66% of studies for specificity. Overall, few studies on the diagnostic accuracy of depression screening tools reported sample size calculations, and the number of patients in most studies was too small to generate reasonably precise accuracy estimates. The failure to provide confidence intervals in published reports may obscure these shortcomings. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.037 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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