A cluster-randomized trial to reduce major perinatal morbidity among women with one prior cesarean delivery in Québec (PRISMA trial): study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Rates of cesarean delivery are continuously increasing in industrialized countries, with repeated cesarean accounting for about a third of all cesareans. Women who have undergone a first cesarean are facing a difficult choice for their next pregnancy, i.e.: (1) to plan for a second cesarean delivery, associated with higher risk of maternal complications than vaginal delivery; or (b) to have a trial of labor (TOL) with the aim to achieve a vaginal birth after cesarean (VBAC) and to accept a significant, but rare, risk of uterine rupture and its related maternal and neonatal complications. The objective of this trial is to assess whether a multifaceted intervention would reduce the rate of major perinatal morbidity among women with one prior cesarean. METHODS/DESIGN: The study is a stratified, non-blinded, cluster-randomized, parallel-group trial of a multifaceted intervention. Hospitals in Quebec are the units of randomization and women are the units of analysis. As depicted in Figure 1, the study includes a 1-year pre-intervention period (baseline), a 5-month implementation period, and a 2-year intervention period. At the end of the baseline period, 20 hospitals will be allocated to the intervention group and 20 to the control group, using a randomization stratified by level of care. Medical records will be used to collect data before and during the intervention period. Primary outcome is the rate of a composite of major perinatal morbidities measured during the intervention period. Secondary outcomes include major and minor maternal morbidity; minor perinatal morbidity; and TOL and VBAC rate. The effect of the intervention will be assessed using the multivariable generalized-estimating-equations extension of logistic regression. The evaluation will include subgroup analyses for preterm and term birth, and a cost-effectiveness analysis. DISCUSSION: The intervention is designed to facilitate: (1) women's decision-making process, using a decision analysis tool (DAT), (2) an estimate of uterine rupture risk during TOL using ultrasound evaluation of low-uterine segment thickness, (3) an estimate of chance of TOL success, using a validated prediction tool, and (4) the implementation of best practices for intrapartum management. TRIAL REGISTRATION: Current Controlled Trials, ID: ISRCTN15346559 . Registered on 20 August 2015.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".