A multi-approach management intervention can lower C-section rate trends: The experience of a Third Level Referral Center
Bibliographic record
Abstract
Objective: To report the experience developed in a Third Level Referral Center in performing a multifaceted intervention strategy to reduce Caesarian Sections (CS) rate. A comparison of our results with the performance of the best Italian hospitals for number of deliveries and CS has been performed.Methods: A monitoring system was set up, based on a prospective collection of all deliveries from 2013 to 2017, according to Robson’s classification. Data have also been collected retrospectively at a regional and national level to compare our results to other institutions. The multi-approach intervention consisted of evidence based tools: process management, training, multiprofessionalism, development of planning and control systems, continuous monitoring, audit and feedback.Results: The percentage of primary CS decreased from 26.71% in 2013 to 15.03% in 2017 (RR adjusted considering the regional average: 0.87 in 2013; 0.57 in 2017, p < .001). A raise of 19.76% in the annual volume of deliveries was registered. Such results have also been confirmed after comparing to the best performing Italian centers. From 2013 to 2016 the percentage of primary CS decreased from 27.02% to 18.04% (RR adjusted considering the regional average: 1.04 in 2013, p > .05; 0.74 in 2016, p < .001), while there was an increase in the annual volume of deliveries from 3,311 to 4,219.Conclusions: Our study confirms that multifaceted interventions can strengthen a continuous quality and safety improvement approach. This is of crucial relevance in the obstetric field and in the Italian country, where overall performance in CS needs to be improved.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".