Quality Assessment of Clinical Practice Guidelines for the Prescription of Antidepressant Drugs During Pregnancy
Bibliographic record
Abstract
Antidepressant use during the gestational period remains a controversial issue. The objective of this study was to appraise the quality of the available clinical practice guidelines (CPGs) that includes recommendations for antidepressant use during pregnancy. We systematically searched for documents published between January 2000 and September 2010 in MEDLINE / TRIP database and on clearing houses and main scientific societies' websites. Four appraisers evaluated each guideline using the Appraisal of Guidelines for Research and Evaluation tool (AGREE II). Intra-class correlation coefficients (ICC) with 95% confidence intervals (CI) were calculated as an overall indicator of agreement. Twelve CPGs were included from a total of 539 references. Only two guidelines were specifically addressed to pregnant women. The overall agreement among reviewers was high (ICC: 0.94, 95% CI: 0.86-0.98). The mean scores and standard deviation (SD) for each of the AGREE II domains were: scope and purpose: 84.4% (12); stakeholder involvement: 67.4% (29.8); rigor of development: 68.6% (19.8); clarity and presentation: 83.4% (17.4); applicability: 44% (37.3); and editorial independence: 62.1% (30.4). After standardizing the scores of the 12 guidelines, 5 were considered as being "recommended", 5 as "recommended with modifications, and 2 as "not recommended". Among the five recommended guidelines, two were specifically conceived to the gestational period. CPGs containing recommendations for antidepressant use during pregnancy were of moderate to high quality. Future guidelines should take into account the observed drawbacks in some domains, and specifically focus a more in depth approach of depression during pregnancy.
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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 | 0.361 | 0.773 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".