Angiogenic factors and the risk of preeclampsia: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The etiological nature of preeclampsia is heterogeneous. The use of biomarkers indices in early pregnancy helps to have appropriate stratification of pregnancies into high- and low risk for the purpose of choosing timely interventions. Objective: The aim of this systematic review was to determine the pathogenic role of soluble soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) inthe prediction of preeclampsia in women. Materials and Methods: We performed a systematic search of the international databases including PubMed, Scopus, and Web of Science until August 2017. The quality of included studies was assessed using the Newcastle-Ottawa Scale. The primary outcome in this review was preeclampsia. The statistical heterogeneity was assessed using the X2 test and quantified by I2. Pooled effects size was obtained by random effects model. Subgroup analyses were also carried out. Results: Totally, 284 records were identified in the initial search and 15 records were finally included in the meta-analysis. The pooled odds ratios (ORs) for the association between the high level of sFlt-1 and low level of PlGF and subsequent development of preeclampsia among women were 5.20 (95% CI: 1.24–9.16) and 2.53 (95% CI: 1.33–3.75), respectively. The mean difference for sFlt-1 and PlGF in women with preeclampsia compared to controls was 1.15 (95% CI: 0.43–1.86) and –0.94 (95% CI: –1.37–0.52),respectively. Conclusion: According to the results from this meta-analysis, increased levels of sFlt-1 and reduced levels of PlGF predict the subsequent development of preeclampsia. Key words: Angiogenic factors, Preeclampsia, sFLT-1, PlGF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".