1432-P: Local Glucose Source for Gestational Diabetes Screening in Resource-Limited Settings
Bibliographic record
Abstract
Background: In resource-limited settings, screening for gestational diabetes (GDM) is not routine. Standard commercial glucose preparations are often not available. Objectives: To test a locally accessible, cheaper alternative glucose source (AGS) for GDM screening in Haiti. Methods: Double cross-over trial of 138 pregnant women 24-28 weeks gestational age. Two one-step 75g oral glucose tolerance tests (oGTT) with capillary blood glucose (CBG) obtained at 0, 1 and 2h were performed 3-5 days apart with the standard glucose drink Glucola and with AGS. Each participant served as her own control. Logistic and linear regression were used to assess AGS-CBG as a predictor of GDM and of Glucola-CBG, respectively. Tolerance of AGS was surveyed. Results: Fourteen women (10%) had GDM, and 5, 2, and 7 were diagnosed based on Glucola-CBG of >92, >180, and >163 mg/dl at 0, 1 and 2h, respectively. At 1 and 2h, mean AGS-CBG vs. Glucola-CBG was 107 vs. 126 (p<.0001) and 89 vs. 113 mg/dl (p<.0001), respectively, and they were positively correlated (r=0.65 and r=0.68, p≤0001). The 1h AGS-CBG had an area under the curve of 0.82 (p=0.0002) to predict GDM. A cut-off of ≥120 mg/dl had a sensitivity, specificity, positive and negative predictive value of 100%, 78%, 25% and 100% to predict GDM not diagnosed by a fasting CBG. Conclusion: Using a fasting CBG of >92 mg/dL and a 1h post-AGS CBG of ≥120 mg/dL, AGS can determine the need for a Glucola-based oGTT, avoiding 3 out of 4 Glucola based tests. Women prefer AGS over Glucola. Disclosure L. Ronciere: None. B. Coriolan: None. R. Destine: None. C. Belanger-Bishinga: None. I. Malhame: None. J.E. von Oettingen: None. Funding McGill University
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".