The utility of first trimester plasma glycated CD59 (pGCD59) in predicting gestational diabetes mellitus: A prospective study of non-diabetic pregnant women in Ireland
Bibliographic record
Abstract
Aims To evaluate the ability of first trimester plasma glycated CD59 (pGCD59) to predict gestational diabetes mellitus (GDM) at 24–28 weeks of gestation. Methods Prospectively, in 378 pregnant women, GDM was diagnosed using the one step 2 h 75 g oral glucose tolerance test adjudicated by the World Health Organisation (WHO) 2013 criteria. The ability of pGCD59 to predict GDM was assessed using receiver operating characteristic (ROC) curves adjusted for maternal age, body mass index (BMI), maternal ethnicity, parity, previous GDM, family history of diabetes mellitus and week of gestation at time of pGCD59 sampling. Results pGCD59 generated an adjusted area under the curve (AUC) of (a) 0.63 (95 %CI:0.56–0.70, p < 0.001) for predicting GDM, and (b) 0.71 (95 %CI:0.62–0.79, p < 0.001 for GDM diagnosed with a fasting plasma glucose (FPG) ≥ 5.1 mmol/L. Sensitivity analysis of BMI subgroups showed that pGCD59 generated the highest AUC in the 35 kg/m 2 ≤ BMI < 40 kg/m 2 (AUC:0.85, 95 %CI:0.70–0.98) and BMI ≥ 40 kg/m 2 (AUC:0.88, 95 %CI:0.63–0.99) categories. Conclusions Early in pregnancy, pGCD59 may be a good predictor of GDM in women with a high BMI and a fair predictor of GDM diagnosed by an elevated FPG independent of BMI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".