Perinatal outcomes for untreated women with gestational diabetes by IADPSG criteria: a population‐based study
Bibliographic record
Abstract
OBJECTIVE: To estimate the risk for adverse perinatal outcomes for women who met the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria but not the two-step criteria for gestational diabetes mellitus (GDM). DESIGN: Population-level cross-sectional study. SETTING: Ontario, Canada. POPULATION: A total of 90 140 women who underwent a 75-g oral glucose tolerance test. METHODS: Women were divided into those who met the diagnostic thresholds for GDM by two-step criteria and were therefore treated, those who met only the IADPSG criteria for GDM and so were not treated, and those who did not have GDM by either criteria. MAIN OUTCOME MEASURES: Hypertensive disorders of pregnancy, preterm delivery, primary caesarean section, large-for-gestational-age, shoulder dystocia and neonatal intensive care unit admission. RESULTS: Women who met the IADPSG criteria had an increased risk for all adverse perinatal outcomes compared with women who did not have GDM. Women with GDM by two-step criteria also had an increased risk of most outcomes. However, their risk for large-for-gestational-age neonates and for shoulder dystocia was actually lower than that of women who met IADPSG criteria. CONCLUSION: Women who met IADPSG criteria but who were not diagnosed with GDM based on the current two-step diagnostic strategy, and were therefore not treated, had an increased risk for adverse perinatal outcomes compared with women who do not have GDM. The current strategy for diagnosing GDM may be leaving women who are at risk for adverse events without the dietary and pharmacological treatments that could improve their pregnancy outcomes. TWEETABLE ABSTRACT: Women who meet IADPSG criteria for GDM have an increased risk for adverse perinatal outcomes compared with women without 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".