Neighborhood Income and Caesarean Section Rates at a Tertiary Care Center in Canada [23A]
Bibliographic record
Abstract
INTRODUCTION: With rising rates of caesarean section (CS) in Canada and worldwide, non-clinical factors for CS warrant consideration. Our objective was to determine the association between a primigravid woman's neighborhood income and rates of CS. METHODS: A retrospective cohort study was conducted at an Ontario tertiary care center from January 2003 to December 2013. Rates of CS were determined using data collected from the Discharge Abstract Database. Women with singleton live births were included. The main exposure variable was neighborhood income quintile. A multivariable model was used to adjust for covariates and provide an estimate of the independent effect of neighborhood income on CS rate. RESULTS: The study cohort comprised 32,714 women. Compared with the lowest quintile, women in the highest quintile had increased rates of CS (Relative Risk (RR) 1.06, 95% Confidence Interval (CI) [1.02, 1.11]). Following adjustment for important confounders, there was no longer an association between neighborhood income and CS rate (adjusted RR 1.00, 95% CI [0.99-1.01]). Women in the highest quintile were more likely to have greater maternal age (P<0.001). CONCLUSION: Although differences in CS rates are seen by neighborhood income quintile, they appear to be related to maternal and infant clinical factors. Neighborhood income does not appear to be an independent predictor of CS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".