Diagnostic accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for detecting major depression in pregnant and postnatal women: protocol for a systematic review and individual patient data meta-analyses
Bibliographic record
Abstract
INTRODUCTION: Studies of the diagnostic accuracy of depression screening tools often used data-driven methods to select optimal cut-offs. Typically, these studies report results from a small range of cut-off points around whatever cut-off score is identified as most accurate. When published data are combined in meta-analyses, estimates of accuracy for different cut-off points may be based on data from different studies, rather than data from all studies for each cut-off point. Thus, traditional meta-analyses may exaggerate accuracy estimates. Individual patient data (IPD) meta-analyses synthesise data from all studies for each cut-off score to obtain accuracy estimates. The 10-item Edinburgh Postnatal Depression Scale (EPDS) is commonly recommended for depression screening in the perinatal period. The primary objective of this IPD meta-analysis is to determine the diagnostic accuracy of the EPDS to detect major depression among women during pregnancy and in the postpartum period across all potentially relevant cut-off scores, accounting for patient factors that may influence accuracy (age, pregnancy vs postpartum). METHODS AND ANALYSIS: Data sources will include Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science. Studies that include a diagnosis of major depression based on a validated structured or semistructured clinical interview administered within 2 weeks of (before or after) the administration of the EPDS will be included. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values. Analyses will evaluate data from pregnancy and the postpartum period separately, as well as combining data from all women in a single model. ETHICS AND DISSEMINATION: This study does not require ethics approval. Dissemination will include journal articles and presentations to policymakers, healthcare providers and researchers. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2015:CRD42015024785.
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".