Investigating the implementation of a Trial of Labour After Caesarean (TOLAC) delivery bundle with respect to decreasing caesarean delivery rates: a multisite quality improvement initiative
Bibliographic record
Abstract
OBJECTIVES: To study the effect of implementing a Trial of Labour After Caesarean (TOLAC) delivery bundle with respect to decreasing caesarean delivery rates across five hospitals. DESIGN: Prospective quality improvement study. SETTING: Five Canadian hospital sites participated, two academic centres and three community hospitals, with annual delivery rates ranging from 2500 to 7500 per site. PARTICIPANTS: All obstetrical patients with a singleton gestation in cephalic presentation and only one previous caesarean delivery were included. INTERVENTIONS: A TOLAC bundle was introduced at each hospital site, consisting of three main interventions: (1) education for healthcare providers, (2) a TOLAC discussion sheet and (3) patient educational tools. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the caesarean delivery rate in eligible patients. Secondary outcomes included rates of trial of labour after caesarean delivery, vaginal birth after caesarean delivery and induction of labour. Balance measures included rates of uterine rupture and neonatal intensive care unit admission. Process measures included attendance at educational rounds, elements of the interventions identified in chart review and view counts for educational videos. RESULTS: The baseline caesarean delivery rate was 77% (1730 out of 2244 eligible patients). Following the introduction of the bundle, the caesarean delivery rate decreased to 71% (1497 out of 2097 eligible patients; 6% decrease, p<0.001). A significant increase in induction rate was noted from 5% preintervention to 9% postintervention (p<0.001). There was no increase in the uterine rupture or neonatal intensive care admission rates. CONCLUSION: This TOLAC bundle can decrease caesarean delivery rates without negatively impacting uterine rupture or neonatal intensive care admission rates. The interventions can be easily adapted for use in different hospitals and practice environments.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".