Provider volume and maternal complications after Caesarean section: results from a population-based study
Bibliographic record
Abstract
BACKGROUND: A large literature search suggests a relationship between hospital/surgeon caseload volume and surgical complications. In this study, we describe associations between post-operative maternal complications following Caesarean section and provider caseload volume, provider years since graduation, and provider specialization, while adjusting for hospital volumes and patient characteristics. METHODS: Our analysis is based on population-based discharge abstract data for the period of April 2004 to March 2014, linked to patient and physician universal coverage registry data. We consider all hospital admissions (N = 20,914) in New Brunswick, Canada, where a Caesarean Section surgery was recorded, as identified by a Canadian Classification of Health Intervention code of 5.MD.60.XX. We ran logistic regression models to identify the odds of occurrence of post-surgical complications during the hospital stay. RESULTS: Roughly 2.6% of admissions had at least one of the following groups of complications: disseminated intravascular coagulation, postpartum sepsis, postpartum hemorrhage, and postpartum infection. The likelihood of complication was negatively associated with provider volume and provider years of experience, and positively associated with having a specialization other than maternal-fetal medicine or obstetrics and gynecology. CONCLUSIONS: Our results suggest that measures of physician training and experience are associated with the likelihood of Caesarean Section complications. In the context of a rural province deciding on the number of rural hospitals to keep open, this suggests a trade off between the benefits of increased volume versus the increased travel time for patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".