Population Demographic Indicators Associated With Incidence of Pyloric Stenosis
Bibliographic record
Abstract
OBJECTIVES: To calculate incidence rates of pyloric stenosis (estimated by the rate of pyloromyotomy) among infants in Ontario and determine their association with population sociodemographic indicators. METHODS: Pyloromyotomy rates were calculated from hospital discharge data from 1993 through 2000. Four-year data (1993-1996 and 1997-2000) were combined to ensure the stability of the rates. Small-area variations in pyloromyotomy rates and correlations between sociodemographic indicators were studied. RESULTS: Approximately 84.0% of the patients were male infants (younger than 1 year). The sex-adjusted pyloromyotomy rates were 1.57 and 1.86 per 1000 with a 3.4-fold and 3.0-fold regional variation in 1993-1996 and 1997-2000, respectively. Urban areas consistently had the lowest pyloromyotomy rate (1.04 and 1.11 per 1000 in Metropolitan Toronto), but the highest rates were from more rural areas (3.30 and 3.38 per 1000 in Quinte, Kingston, Rideau). After adjusting for socioeconomic status and availability of surgeons in the region, living in a rural area remained a significant factor associated with a higher incidence of pyloromyotomy. The risk of pyloromyotomy for an infant who lives in a region with more than two thirds of its area classified as rural was 1.79 (95% confidence interval, 1.23-2.61; P<.005). CONCLUSIONS: The observed changes in incidence and a higher rate among male infants are consistent with results from previous comparative studies conducted in North America and Sweden. The rural/urban differences suggest that environmental influences related to living in these areas may have a role in the etiology of pyloric stenosis. Further research is needed to evaluate these differences.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".