Does having cesarean section capability make a difference to a small rural maternity service?
Bibliographic record
Abstract
OBJECTIVE: To determine whether having cesarean section capability in an isolated rural community makes a difference in adverse maternal or perinatal outcomes. DESIGN: Retrospective study comparing population-based obstetric outcomes of two rural remote hospitals in northwestern British Columbia. One hospital had cesarean section capability; one did not. SETTING: Bella Coola General Hospital (with cesarean section capability) in Bella Coola Valley (BCV) and Queen Charlotte Islands General Hospital (without cesarean section capability) in Queen Charlotte City (QCC). PARTICIPANTS: Women who carried pregnancies beyond 20 weeks' gestation and who gave birth between January 1, 1986, and December 31, 2000. INTERVENTIONS: British Columbia Vital Statistics Agency data was used to compare obstetric outcomes in the two communities. A chart audit of local births at BCV and QCC was done to validate the vital statistics data. MAIN OUTCOME MEASURES: Perinatal death, newborn transfer to a tertiary care facility, birth weight, gestational age at delivery, mode of delivery, and Apgar score. RESULTS: The rate of preterm deliveries in QCC was higher (relative risk 1.41, 95% confidence interval 1.00 to 1.99; P = .047) than the rate in BCV. Otherwise, there were no differences in adverse maternal or perinatal outcomes in the two populations. In BCV, 69.8% of women delivered locally compared with 50.2% of women in the southern Queen Charlotte Islands (P < .001). CONCLUSION: Having local cesarean section capability is associated with a greater proportion of local deliveries and a lower rate of preterm deliveries.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".