Pregnancy-associated plasma protein A (PAPP-A) as a first trimester serum biomarker for preeclampsia screening: a systematic review and meta-analysis
Bibliographic record
Abstract
The aim of this study is to systematically examine the role of the pregnancy-associated plasma protein A (PAPP-A) serum biomarker in the first trimester screening of preeclampsia (PE). A systematic search of the literature was conducted on PubMed via Medline, and Cochrane Library up to 8 November 2022, for prospective studies evaluating PAPP-A serum levels in first trimester pregnant women as a screening biomarker for PE. Eligible were all prospectively designed case-control or cohort studies, published in English. Two investigators independently examined the studies and the studies’ characteristics were extracted. Newcastle-Ottawa Scale (NOS) for case-control and cohort studies were applied to assess the risk of bias. For the quantitative analysis of the studies, a meta-analysis was also performed. A total of 22 studies including 33,651 pregnant women were assessed, of whom, 2001 were diagnosed with PE. A meta-analysis was performed, showing that PAPP-A levels in the first trimester were significantly lower in early onset preeclamptic women (MD: −0.24, 95% CI: −0.37, −0.11, p = .0002), late onset (MD: −0.15, 95% CI: −0.25, −0.05, p = .03), and total preeclamptic cases (MD = −0.17, 95% CI = −0.23, −0.11, p < .00001) when compared with controls. Our results suggest that PAPP-A can be a promising predictor in early screening for PE; hence, women at risk can be diagnosed early in their pregnancy stage and benefit from individualized PE treatment before it progresses.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".