High level of homocysteine is associated with pre-eclampsia risk in pregnant woman: a meta-analysis
Bibliographic record
Abstract
We aimed to investigate the correlation between blood homocysteine (Hcy) levels and pre-eclampsia (PE) risk in pregnant women. Related articles were searched using PubMed, Embase, and Web of Science databases. Methodological quality of included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS). Cochran's Q and I2 tests were used to evaluate heterogeneity. Egger’s test was used to evaluate publication bias. A sensitivity analysis was performed to test stability of the results using a one-by-one elimination method. Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to assess certainty of evidence. Nine studies (4384 PE and 26021 non-PE patients) were included in the meta-analysis. The methodology of them was of good quality, with NOS scores of 5–8. However, there was a significant heterogeneity among included studies. Therefore, the random effect model was generated and combined results suggested a significant association between increased level of Hcy in pregnant women and PE risk. Although a significant publication bias was found in the current study with a P value of 0.006 in the Egger test, sensitivity analysis showed that the combined results were stable and did not vary significantly from any single study. However, the GRADE evidence quality was very low, which may lower the recommendation of pooled results. Increased levels of Hcy in maternal blood were significantly associated with the risk of PE, but low certainty of evidence need to be improved by more high-quality studies.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.060 |
| Bibliometrics | 0.007 | 0.006 |
| 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".