Transparency and completeness of reporting of depression screening tool accuracy studies: A meta‐research review of adherence to the Standards for Reporting of Diagnostic Accuracy Studies statement
Bibliographic record
Abstract
OBJECTIVES: Accurate and complete study reporting allows evidence users to critically appraise studies, evaluate possible bias, and assess generalizability and applicability. We evaluated the extent to which recent studies on depression screening accuracy were reported consistent with Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement requirements. METHODS: MEDLINE was searched from January 1, 2018 through May 21, 2021 for depression screening accuracy studies. RESULTS: 106 studies were included. Of 34 STARD items or sub-items, the number of adequately reported items per study ranged from 7 to 18 (mean = 11.5, standard deviation [SD] = 2.5; median = 11.5), and the number inadequately reported ranged from 3 to 17 (mean = 10.1, SD = 2.5; median = 10.0). There were eight items adequately reported, seven partially reported, 11 inadequately reported, and four not applicable in ≥50% of studies; the remaining four items had mixed reporting. Items inadequately reported in ≥70% of studies related to the rationale for index test cut-offs examined, missing data management, analyses of variability in accuracy results, sample size determination, participant flow, study registration, and study protocol. CONCLUSION: Recently published depression screening accuracy studies are not optimally reported. Journals should endorse and implement STARD adherence.
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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 | 0.589 | 0.828 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| 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".