The utility of plasma glycated CD59 in predicting postpartum glucose intolerance: A prospective study of women diagnosed with GDM during a period of universal GDM screening
Bibliographic record
Abstract
AIMS: Gestational diabetes (GDM) is associated with the development of postpartum (PP) glucose intolerance. Plasma glycated CD59 (pGCD59) is an emerging biomarker for the detection of hyperglycaemia. The aim of this study was to assess the ability of PP pGCD59 to predict the development of PP GI as defined by the 2 h 75 g OGTT using the ADA criteria, in a cohort of women diagnosed with prior GDM in the index pregnancy using the 2 h 75 g OGTT at 24-28 weeks of gestation according to the World Health Organization (WHO) 2013 criteria. METHODS: Of the 2017 pregnant women recruited prospectively 140 women with gestational diabetes had samples for pGCD59 taken PP at the time of the OGTT. The ability of pGCD59 to predict the results of the PP OGTT was assessed using nonparametric receiver operating characteristic (ROC) curves. RESULTS: Women with PP glucose intolerance had significantly higher PP pGCD59 levels compared to women with normal glucose tolerance PP (3.8 vs. 2.7 SPU). PP pGCD59 identified women who developed glucose intolerance PP with an AUC of 0.80 (95% CI: 0.70-0.91). A PP pGCD59 cut-off value of 1.9 SPU generated a sensitivity of 100% (95% CI: 83.9-100), specificity of 16.9% (95% CI: 9.8-26.3), positive predictive value of 22.1% (95% CI: 21.0-22.6), and negative predictive value of 100% (95% CI: 87.4-100). PP fasting plasma glucose generated an AUC of 0.96 (95% CI: 0.89-0.99) for the identification of PP glucose intolerance. CONCLUSION: Our study found that PP pGCD9 may be a promising biomarker to identify women not requiring PP glucose intolerance screening using the traditional OGTT. While the diagnostic accuracy of pGCD59 is good, fasting plasma glucose remains a better test for the identification of PP glucose intolerance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".