Prepregnancy Diabetes and Perinatal Mental Illness: A Population-Based Latent Class Analysis
Bibliographic record
Abstract
We examined the risk of any perinatal mental illness associated with prepregnancy diabetes and identified how diabetes duration, complexity, and intensity of care affect this risk. We performed a population-based study of women aged 15-49 years with (n = 14,186) and without (n = 843,818) prepregnancy diabetes who had a singleton livebirth (Ontario, Canada, 2005-2015) and no recent mental illness. Modified Poisson regression estimated perinatal mental illness risk between conception and 1 year postpartum in women with versus without diabetes and in diabetes groups, defined by a latent class analysis of diabetes duration, complexity, and intensity-of-care variables, versus women without diabetes. Women with diabetes were more likely than those without to develop perinatal mental illness (18.1% vs. 16.0%; adjusted relative risk = 1.11, 95% confidence interval: 1.07, 1.15). Latent classes of women with diabetes were: uncomplicated and not receiving regular care (59.7%); complicated, with longstanding diabetes, and receiving regular care (16.4%); and recently diagnosed, with comorbidities, and receiving regular care (23.9%). Perinatal mental illness risk was elevated in all classes versus women without diabetes (adjusted relative risks: 1.09-1.12), but results for class 2 were nonsignificant after adjustment. Women with diabetes could benefit from preconception and perinatal strategies to reduce their mental illness risk.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".