Inconsistent Evidence: Analysis of Six National Guidelines for Vaginal Birth After Cesarean Section
Bibliographic record
Abstract
BACKGROUND: Guidelines are increasingly used to direct clinical practice, with the expectation that they improve clinical outcomes and minimize health care expenditure. Several national guidelines for vaginal birth after cesarean section (VBAC) have been released or updated recently, and their range has created dilemmas for clinicians and women. The purpose of this study was to summarize the recommendations of existing guidelines and assess their quality using a standardized and validated instrument to determine which guidelines, if any, are best able to guide clinical practice. METHODS: English language guidelines on VBAC were purposively selected from national and professional organizations in the United Kingdom, United States, Canada, New Zealand, and Australia. The Appraisal of Guidelines for Research and Evaluation (AGREE) instrument was applied to each guideline, and each was analyzed to determine the range and level of evidence on which it was based and the recommendations made. RESULTS: Six guidelines published or updated between 2004 and 2007 were examined. Only two of the six guidelines scored well overall using the AGREE instrument, and the evidence used demonstrated great variety. Most guidelines cited expert opinion and consensus as evidence for some recommendations. Reported success rates for VBAC ranged from 30 to 85 percent, and reported rates of uterine rupture ranged from 0 to 2.8 percent. CONCLUSIONS: VBAC guidelines are characterized by quasi-experimental evidence and consensus-based recommendations, which lead to wide variability in recommendations and undermine their usefulness in clinical 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 | 0.166 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".