VBAC or elective caesarean? a midwife-led shared decision making approach to choice following a caesarean section
Bibliographic record
Abstract
Background Women with one prior caesarean face a choice between an attempted vaginal birth after caesarean (VBAC) or a caesarean section (CS). There is no evidence that either choice is associated with better outcomes. NICE states that women's preferences should guide the choice. In 2010 a midwife-led shared decision making (SDM) approach was introduced into the care pathway of women with one prior CS. This aim of this study was to evaluate this approach. Implementation 103 women were sent a decision aid (information leaflet and Ottawa Decision Guide) prior to a VBAC clinic appointment at 20 weeks where mode of delivery was discussed / agreed by a midwife trained in SDM. A questionnaire was administered after the consultation ascertaining involvement in the decision making process. Results 91% of women reported they had engaged with the decision aid. 42% opted for VBAC, 31% for ELCS, and 27% deferred the decision to 36 weeks (when 46% chose VBAC). Of 75 women who had made a decision at 20 weeks, 6 changed their choice at 36 weeks (all from VBAC to CS). 47% returned questionnaires; 62/38% strongly agreed/agreed they had been given the pros and cons of each choice; 76/24% strongly agreed/agreed that they had been involved in decisions and 74/26% strongly agreed/agreed that they were involved as much as they wanted to be. Conclusion Results suggest that women engaged with the decision aids and benefited from a SDM approach. The small number of women who altered their decision at 36 weeks suggests decision quality may be better when a SDM approach is adopted.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".