Associations of pregnancy-associated plasma protein-A level with essential hypertension and hypertensive disorders in pregnancy in Chinese population: a meta-analysis of 20 research studies involving 3332 individuals
Bibliographic record
Abstract
OBJECTIVE: To explore the associations between serum pregnancy-associated plasma protein-A (PAPP-A) level, and essential hypertension (EH) and hypertensive disorders in pregnancy (HDP) in Chinese population. METHODS: Pertinent studies were independently searched in PubMed, Embase, Cochrane Library, Chinese Biomedical Database (CBM), Wanfang databases and China National Knowledge Infrastructure (CNKI). The standardised mean difference (SMD) with 95% CIs was used to estimate the size of the effect. The subgroup analyses and meta-regression analysis were performed to identify the sources of heterogeneity among studies. Sensitivity analysis was conducted to assess the stability of the results. The publication bias between studies was examined by using Begg's funnel plots and Egger's test. RESULTS: A total of 20 studies involving 1493 patients and 1839 controls were included in the current meta-analysis. The PAPP-A level was significantly higher in EH patients than in controls (SMD=1.960, 95% CI 1.305 to 2.615, p<0.001), and significant associations were observed in all subgroups. The PAPP-A level was also significantly higher in HDP patients than in healthy pregnant women (SMD=2.249; 95% CI 1.324 to 3.173, p<0.001). The positive association between PAPP-A level and the risk of HDP was consistently observed in all subgroups except the subgroup with low NOS score. CONCLUSIONS: The present meta-analysis suggests that an elevated PAPP-A level may be associated with susceptibilities to EH and HDP.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.058 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".