Comparative Efficacy of the Generalized Anxiety Disorder 7-Item Scale and the Edinburgh Postnatal Depression Scale as Screening Tools for Generalized Anxiety Disorder in Pregnancy and the Postpartum Period
Bibliographic record
Abstract
OBJECTIVE: About 24.1% of pregnant women suffer from at least 1 anxiety disorder, 8.5% of whom suffer specifically from generalized anxiety disorder (GAD). GAD is often associated with major depressive disorder (MDD). During the perinatal period, the presence of physical and somatic symptoms often makes differentiation between depression and anxiety more challenging. To date, no screening tools have been developed to detect GAD in the perinatal population. We investigated the psychometric properties of the GAD 7-item Scale (GAD-7) as a screening tool for GAD in pregnant and postpartum women. METHODS: Two hundred and forty perinatal women (n = 155 pregnant and n = 85 postpartum) referred for psychiatric consultation were enrolled. On the day of initial assessment, all women completed the GAD-7 and the Edinburgh Postnatal Depression Scale (EPDS). Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-based diagnoses were made by experienced psychiatrists. Scores from the GAD-7 and EPDS were compared with the clinical diagnoses to evaluate the psychometric properties of the GAD-7 and EPDS when used as a screening tool for GAD. RESULTS: The GAD-7 yielded a sensitivity of 61.3% and specificity of 72.7% at an optimal cut-off score of 13. Compared with the EPDS and the EPDS-3A subscale, the GAD-7 displayed greater accuracy and specificity over a greater range of cut-off scores and more accurately identified GAD in patients with comorbid MDD. CONCLUSION: Our findings suggest that the GAD-7 represents a clinically useful scale for the detection of GAD in perinatal women.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".