Rates and Risk Factors for Recurrence of Gestational Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine the recurrence rate of gestational diabetes (GDM) during a subsequent pregnancy among women who had GDM during an index pregnancy and to identify factors associated with the probability of recurrence RESEARCH DESIGN AND METHODS: A retrospective longitudinal study was performed in Nova Scotia, Canada, of women who were diagnosed as having GDM during a pregnancy between the years of 1980 and 1996 and who had at least one subsequent pregnancy during this time period. When only the index and first subsequent pregnancy were analyzed, the cohort included 651 women. The recurrence rate of GDM in the pregnancy after the pregnancy with the initial diagnosis of GDM was determined. Multivariate regression models were constructed to model the recurrence of GDM in a subsequent pregnancy as functions of potential predictors to estimate RRs and CIs. RESULTS: The rate of recurrence of GDM in the pregnancy subsequent to the index pregnancy was found to the 35.6% (95% CI = 31.9-39.3%). Multivariate regression models showed that infant birth weight in the index pregnancy and maternal prepregnancy weight before the subsequent pregnancy were predictive of recurrent GDM. CONCLUSIONS: In this large cohort of women, slightly more than one-third of the subjects had diabetes in a subsequent pregnancy, which is consistent with recurrence rates in other predominately white populations. Strategies to reduce the occurrence of neonatal macrosomia and maternal prepregnancy obesity may help lower the rate of recurrence of GDM.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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".