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
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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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 | 0.262 | 0.593 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".