Is platelet to lymphocyte ratio predictive of preeclampsia? A systematic review and meta-analysis
Bibliographic record
Abstract
Background: To evaluate the value of the platelet-to-lymphocyte ratio (PLR) in predicting preeclampsia (PE) in pregnant women.Methods: PubMed, EMBASE and Web of Science databases were searched for observational studies (cohort, case-control or cross-sectional) that reported pre-treatment maternal PLR values in women with and without PE.The analysis was done using a random effects model.Pooled effect sizes were reported as weighted mean difference (WMD) with 95% confidence intervals (CIs).Newcastle-Ottawa Scale (NOS) was used to evaluate the risk of bias.Results: Twenty-five studies with 7755 patients were included in this meta-analysis.PLR was comparable in patients with PE and healthy pregnant women (WMD -2.97; 95% CI: -11.95 to 6.02; N ¼ 16).Patients with mild (WMD -3.00; 95% CI: -17.40 to 11.41; N ¼ 12) and severe PE (WMD -5.77; 95% CI: -25.48 to 13.94; N ¼ 14) had statistically similar PLR, compared to healthy controls.Conclusions: Our findings show similar PLR in PE and healthy pregnancies.PLR, therefore, may not be used to differentiate between PE and normal pregnancy or for assessing the severity of PE.The majority of included studies were case-control, potentially introducing bias, and we identified evidence of publication bias as well.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".