First trimesters Pregnancy-Associated Plasma Protein-A levels value to Predict Gestational diabetes Mellitus: A systematic review and meta-analysis of the literature
Bibliographic record
Abstract
Detecting pregnant women at risk of diabetes in first months can help them by early intervention for delaying or preventing onset of GDM. In this study, we aimed to assess the Predictive value of first trimester Pregnancy related plasma protein-A (PAPP-A) levels for detecting Gestational diabetes Mellitus (GDM). This systematic review and meta-analysis was conducted through probing in databases. PubMed, Scopus, Medline and Google scholar citations were searched to find the published papers from 1974 to 2017. Studies were considered eligible if they were cohorts, case-control studies, reported GDM result, not other types, conducted on singleton pregnancy, measured Serum pregnancy associated plasma protein A in the first trimester and evaluated the relation of first trimester pregnancy associated plasma protein-A and GDM. Two reviewers independently assessed the quality with Newcastle-Ottawa and extracted data in the Pre-defined checklist. Analysis of the data was carried out by "Comprehensive Meta-analysis Version 2 (CAM)" and Metadisc software. 17 articles have our inclusion criteria and were considered in our systematic review, 5 studies included in Meta-analysis. Meta-analysis of these articles showed that the predictive value of PAPP-A for GDM has 55% sensitivity (53-58), 90% (89-90) specificity, LR + 2.48 (0.83-7.36) and LR - 0.70 (0.45-1.09) with 95% confidence intervals. In our study PAPP-A has low predictive accuracy overall, but it may be useful when combined with other tests, and this is an active part for future research. One limitation of our study is significant heterogeneity because of different adjusted variables and varied diagnostic criteria.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".