Falling Third Trimester Insulin Requirements and Adverse Pregnancy Outcomes in Individuals with Pre-Existing Diabetes: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective: To determine whether a third-trimester drop in insulin requirements in pregnant people with pre-existing diabetes is associated with a subsequent occurrence of adverse pregnancy outcomes. Research Design and Methods: We conducted a retrospective cohort study of patients with type 1 and 2 diabetes who were followed at a tertiary referral center in Toronto, Canada. We collected data on insulin dosing in the third trimester (after 28 weeks of pregnancy) and compared outcomes in those with and without a third-trimester drop of 15% or more in their total insulin requirements. Our primary outcome was a composite of stillbirth, spontaneous preterm birth or preterm premature rupture of membranes, and iatrogenic preterm birth or cesarean birth for fetal wellbeing concerns, occurring following the drop in insulin requirements. We conducted regression analyses controlling for early pregnancy glycosylated hemoglobin, body mass index, and diabetes-related microvascular disease, and presented results as odds ratios (OR) with 95% confidence intervals (95%CI). Results: We included 350 pregnant people—146 with type 1 and 204 with type 2 diabetes. Of these, 54 (15.4%) had a third-trimester drop of 15% or more in their total insulin requirements. There was no difference in the primary outcome between groups (OR 0.97; 95% CI 0.41–2.10). Conclusions: Based on this single-center study, limited by sample size and analytic constraints, in people with pre-existing diabetes, a third-trimester drop of ≥15% in total insulin requirements was not associated with subsequent occurrence of adverse pregnancy outcomes. Larger prospective studies looking at associations between a drop in insulin requirements and subsequent occurrence of adverse pregnancy outcomes are necessary to inform meta-analyses and clinical decision making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".