Treatment of women with mild gestational diabetes mellitus decreases the risk of adverse perinatal outcomes
Bibliographic record
Abstract
AIMS: Glycemic thresholds used to diagnose gestational diabetes mellitus (GDM) are a continued subject of debate. Lower glycemic thresholds identify women with milder GDM for whom treatment benefit is unclear. We compared adverse maternal and neonatal outcomes in treated and untreated women with mild hyperglycemia. METHODS: We reviewed 11 553 patient charts from two tertiary care centers and included singleton pregnancies >32-week gestation. GDM was diagnosed using the one- or two-step 75 g oral glucose tolerance test (OGTT) depending on the center. All OGTT results were reviewed. Women with glycemic values falling between the thresholds of the two tests, referred to as intermediate hyperglycemic (IH), defined as FPG 5.1-5.2 mmol/L, 1 h PG 10.0-10.5 mmol/L, or 2 h PG 8.5-8.9 mmol/L at 75 g OGTT, were untreated at center A and treated at center B. RESULTS: There were 630 women with IH, 334 were untreated (center A) and 296 who were treated (center B). After adjusting for covariates, untreated IH women had significantly higher rates of gestational hypertension (aOR 6.02, P = 0.002), large for gestational age (LGA) (aOR 3.73, P < 0.001) and birthweights > 4000 g (aOR 3.35, P = 0.001). Our results indicate that treating 11 women with IH would prevent one LGA birth and treating 13 would prevent 1 birthweight > 4000 g. CONCLUSION: The diagnosis of GDM using the two-step OGTT fails to identify subgroups of women with mild hyperglycemia that would benefit from treatment to lower the risk for adverse maternal and neonatal outcomes. Treatment of women with mild hyperglycemia decreased the risk of LGA and birthweight >4000 g by 3-fold.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".