Effect of multiparity and ethnicity on the risk of development of diabetes: a large population‐based cohort study
Bibliographic record
Abstract
AIMS: To investigate the relationship between increasing parity and diabetes in a large, population-based cohort, and to examine if this relationship is different among high-risk ethnic groups. METHODS: A population-based, retrospective cohort study was performed in 738 440 women aged 18-50 years, who delivered babies in Ontario between 1 April 2002 and 31 March 2011. Diabetes incidence postpartum was calculated for each parity and ethnic group. A multivariable analysis of the effect of parity and ethnicity on the incidence of diabetes was performed using a Cox proportional hazards model, adjusting for confounders. RESULTS: The diabetes incidence rate per 1000 person-years was 3.69 in women with 1 delivery, 4.12 in women with 3 deliveries and 7.62 in women with ≥5 deliveries. Women with ≥3 deliveries had a higher risk of developing diabetes compared with women with 1 delivery [adjusted hazard ratios 1.06 (95% CI 1.01-1.11) for 3 deliveries, 1.33 (95% CI 1.25-1.43) for 4 deliveries and 1.53 (95% CI 1.41-1.66) for ≥5 deliveries). A similar rise in risk could be seen in Chinese and South-Asian women, with the most influence in Chinese women [hazard ratio 4.59 (95% CI 2.36-8.92) for ≥5 deliveries]. CONCLUSIONS: There was a positive and graded relationship between increasing parity and risk of development of diabetes. The influence of parity was seen in all ethnicities. This association may be partly related to increasing weight gain and retention with increasing parity, or deterioration in β-cell function. This merits further exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".