Perinatal risk factors for childhood Type 1 diabetes in Western Australia—a population‐based study (1980–2002)
Bibliographic record
Abstract
AIMS: To investigate perinatal risk factors for childhood Type 1 diabetes in Western Australia, using a complete population-based cohort. METHODS: Children born between 1980 and 2002 and diagnosed with Type 1 diabetes aged < 15 years (n = 940) up to 31 December 2003 were identified using a prospective population-based diabetes register with a case ascertainment rate of 99.8%. Perinatal data were obtained for all live births in Western Australia from 1980 to 2002 (n = 558 633) and record linkage performed to identify the records of cases. RESULTS: The incidence of Type 1 diabetes increased by 13% for each 5-year increase in maternal age [adjusted incidence rate ratio (IRR) 1.13, 95% confidence interval (CI) 1.05, 1.21], by 13% for every 500-g increase in birth weight (adjusted IRR 1.13, 95% CI 1.04, 1.23). The incidence decreased with increasing birth order (adjusted IRR 0.89, 95% CI 0.82, 0.96) and increasing gestational age (adjusted IRR 0.84, 95% CI 0.77, 0.93). A higher incidence of Type 1 diabetes was associated with an urban vs. non-urban maternal address at the time of birth (adjusted IRR 1.38, 95% CI 1.18, 1.63), but no association was found with socio-economic status of the area. CONCLUSIONS: A higher incidence of Type 1 diabetes was associated with increasing maternal age, higher birth weight, lower gestational age, lower birth order and urban place of residence at the time of birth.
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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 | 0.001 |
| 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".