Risk of Bias from Inclusion of Currently Diagnosed or Treated Patients in Studies of Depression Screening Tool Accuracy: A Cross-Sectional Analysis of Recently Published Primary Studies and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Depression screening can improve upon usual care only if screening tools accurately identify depressed patients who would not otherwise be recognized by healthcare providers. Inclusion of patients already being treated for depression in studies of screening tool accuracy would inflate estimates of screening accuracy and yield. The present study investigated (1) the proportion of primary studies of depression screening tool accuracy that were recently published in journals listed in MEDLINE, which appropriately excluded currently diagnosed or treated patients; and (2) whether recently published meta-analyses identified the inclusion of currently diagnosed or treated patients as a potential source of bias. METHODS: MEDLINE was searched from January 1, 2013 through March 27, 2015 for primary studies and meta-analyses on depression screening tool accuracy. RESULTS: Only 5 of 89 (5.6%) primary studies excluded currently diagnosed or treated patients from any analyses and only 3 (3.4%) from main analyses. In 3 studies that reported the number of patients excluded due to current treatment, the number of excluded patients was more than twice the number of newly identified depression cases. None of 5 meta-analyses identified the inclusion of currently diagnosed and treated patients as a potential source of bias. CONCLUSIONS: The inclusion of currently diagnosed and treated patients in studies of depression screening tool accuracy is a problem that limits the applicability of research findings for actual clinical practice. Studies are needed that evaluate the diagnostic accuracy of depression screening tools among only untreated patients who would potentially be screened in practice.
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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 | 0.252 | 0.406 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".