Repeat elective caesarean: decision-making for women with a previous caesarean section
Bibliographic record
Abstract
Context: Among women with a prior caesarean section, 82.2% will have another caesarean delivery. The Society of Obstetrics and Gynaecology of Canada (SOGC) recommends that physicians offer medically eligible women with a previous caesarean section a trial of labour, to attempt a vaginal delivery. With greater inclusion of the patient in medical decision-making, it is important to understand women's part in this decision-making process. Objectives: To describe women's decision-making by looking at: 1) whether the decision was reported as primarily physician- or patient-driven 2) women's reasons for repeat caesarean section, 3) women's main information sources. Methods: For one year women booked for a repeat elective caesarean section, who were eligible for a trial of labour according to the 2005 guidelines of the SOGC, were approached with the survey in hospital post-partum, and invited to participate in the study. Chart review was used to determine eligibility, and obtain other medical characteristics. Results: Most of the women (77 %) reported being involved in the decision about their caesarean section. However, almost a quarter reported wholly physician-driven decisions (23 %). The main reasons women selected for a caesarean section related to their previous birth experience, and the physician's recommendation. Women born outside of Canada, with less education or who were allophones, were less likely to report using certain information sources, such as the Internet, and to find the information in the hospital-provided pamphlet useful. All in all, the women who received less information were more likely to report solely physician-driven decisions. Conclusion: Although patient involvement in decision-making is the norm, some decisions for caesarean section are made without the patient. Women's concerns, such as fear of a failed vaginal delivery, play an important role in this decision-making. Overall, immigrant women may understand less about their birth options than their Canadian peers. Addressing these concerns during pre-natal counselling may aid more fully informed consent, help assuage women's fears of vaginal birth and may increase the number of women attempting a trial of labour.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".