CX3CL1/Fractalkine as a biomarker for early pregnancy prediction of preterm premature rupture of membranes
Bibliographic record
Abstract
Abstract Objectives The objective of our study was to evaluate serum CX3CL1/Fractalkine, a monocyte/macrophage chemoattractant expressed in cytotrophoblasts and decidual cells, as a predictive biomarker for the occurrence of preterm premature rupture of membranes (PPROM). Methods A case-control study of 438 pregnancies including 82 PPROM cases and 64 preterm labor with intact membranes cases with blood samples collected at first trimester, second trimester and delivery was conducted. The predictive ability of CX3CL1 and maternal risk factors for the occurrence of PPROM was assessed by receiver operating characteristic curve analysis. A second, independent cohort was prospectively constituted to confirm the case-control study results. Results First trimester CX3CL1 was significantly increased in PPROM cases when compared to matched controls. Multivariate regression analysis highlighted a significant difference for CX3CL1 measured during the first trimester (p<0.001). Alone, CX3CL1 predicts PPROM with a 90 % sensitivity and a specificity around 40 %. The area under the receiver operating characteristic curve for PPROM prediction were 0.64 (95% confidence interval: 0.57–0.71) for first trimester CX3CL1, and 0.61 (95% confidence interval: 0.54–0.68) for maternal risk factors (body mass index<18.5 kg/m2, nulliparity, tobacco use and the absence of high school diploma). The combination of CX3CL1 and maternal risk factors significantly improved the area under the curve: 0.72 (95% confidence interval: 0.66–0.79) (p<0.001). The results were confirmed on a second independent cohort. Conclusions CX3CL1 is a promising blood biomarker in the early (first trimester) prediction of PPROM.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".