Plasma Glycated <scp>CD59</scp> and Gestational Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
AIMS: Gestational diabetes mellitus (GDM) is a common complication of pregnancy worldwide. The standard method for screening GDM is the 75 g oral glucose tolerance test (OGTT). However, the OGTT is difficult, time-consuming and requires fasting, making it an inconvenient test for GDM. Researchers have turned their attention to alternative biomarkers for GDM. This study aimed to systematically investigate the potential of plasma glycated CD59 (pGCD59) as a new biomarker for GDM and its associated adverse pregnancy outcomes. METHODS: The systematic review was performed in the PubMed, ISI Web of Science, Scopus and Google Scholar databases from 1/1/2000 to 4/1/2024, and relevant studies were selected based on the inclusion and exclusion criteria. The quality of the studies was assessed using the Newcastle-Ottawa scale. RESULTS: The study revealed that pGCD59 levels before 20 weeks and during the second trimester of pregnancy have the potential to predict the results of the OGTT and also forecast adverse pregnancy outcomes, such as postpartum glucose intolerance (PP GI), neonatal hypoglycaemia (NH) and having large for gestational age (LGA) infants. The predictive ability of pGCD59 was found to be affected by the GDM status, especially body mass index (BMI). CONCLUSIONS: In conclusion, pGCD59 may be a promising indicator of glucose levels and could serve as a new biomarker for GDM. However, additional studies are needed to establish a reliable reference range and cut-off value for pGCD59.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".