Developing and pre-testing a decision board to facilitate informed choice about delivery approach in uncomplicated pregnancy
Bibliographic record
Abstract
BACKGROUND: The rate of caesarean sections is increasing worldwide, yet medical literature informing women with uncomplicated pregnancies about relative risks and benefits of elective caesarean section (CS) compared with vaginal delivery (VD) remains scarce. A decision board may address this gap, providing systematic evidence-based information so that patients can more fully understand their treatment options. The objective of our study was to design and pre-test a decision board to guide clinical discussions and enhance informed decision-making related to delivery approach (CS or VD) in uncomplicated pregnancy. METHODS: Development of the decision board involved two preliminary studies to determine women's preferred mode of risk presentation and a systematic literature review for the most comprehensive presentation of medical risks at the time (VD and CS). Forty women were recruited to pre-test the tool. Eligible subjects were of childbearing age (18-40 years) but were not pregnant in order to avoid raising the expectation among pregnant women that CS was a universally available birth option. Women selected their preferred delivery approach and completed the Decisional Conflict Scale to measure decisional uncertainty before and after reviewing the decision board. They also answered open-ended questions reflecting what they had learned, whether or not the information had helped them to choose between birth methods, and additional information that should be included. Descriptive statistics were used to analyse sample characteristics and women's choice of delivery approach pre/post decision board. Change in decisional conflict was measured using Wilcoxon's sign rank test for each of the three subscales. RESULTS: The majority of women reported that they had learned something new (n = 37, 92%) and that the tool had helped them make a hypothetical choice between delivery approaches (n = 34, 85%). Women wanted more information about neonatal risks and personal experiences. Decisional uncertainty decreased (p < 0.001) and perceived effectiveness of decisions increased (p < 0.001) post-intervention. CONCLUSION: Non-pregnant women of childbearing age were positive about the decision board and stated their hypothetical delivery choices were informed by risk presentation, but wanted additional information about benefits and experiences. This study represents a preliminary but integral step towards ensuring women considering delivery approaches in uncomplicated pregnancies are fully informed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".