Protocol for the evaluation of a decision aid for women with a breech-presenting baby [ISRCTN14570598]
Bibliographic record
Abstract
BACKGROUND: There is now good evidence about the management options for pregnant women with a breech presentation (buttocks or feet rather than head-first) at term; external cephalic version (ECV) - the turning of a breech baby to a head-down position and/or planned caesarean section (CS). Each of these options has benefits and risks and the relative importance of these vary for each woman, subject to her personal values and preferences, a situation where a decision aid may be helpful.Decision aids are designed to assist patients and their doctors in making informed decisions using information that is unbiased and based on high quality research evidence. Decision aids are non-directive in the sense that they do not aim to steer the user towards any one option, but rather to support decision making which is informed and consistent with personal values.The ECV decision aid was developed using the Ottawa Decision Support Framework, including a systematic review of the evidence about the benefits and risks of the options for breech pregnancy. It comprises an audiotape with a supplementary booklet and worksheet, a format that can be taken home and discussed with a partner. This project aims to evaluate the ECV decision aid for women with a breech presenting baby in late pregnancy. STUDY DESIGN: We aim to evaluate the effectiveness of the decision aid compared with usual care in a randomised controlled trial in maternity hospitals that offer ECV. The study group will receive the decision aid in addition to usual care and the control group will receive standard information on management options for breech presentation from their usual pregnancy care provider. Approximately 184 women with a single breech-presenting baby at greater than 34 weeks gestation and who are clinically eligible for ECV will be recruited for the trial.The primary outcomes of the study are knowledge, decisional conflict, anxiety and satisfaction with decision-making that will be assessed using self-administered questionnaires. The decision aid is not intended to influence either the uptake of either ECV or planned CS, however we will monitor health service utilisation rates and maternal and perinatal outcomes.
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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 | 0.063 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.245 | 0.044 |
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".