Interleukin-6 bedside testing in women at high risk of preterm birth
Bibliographic record
Abstract
Objective Infection is likely to contribute to the complex aetiology of preterm birth (PTB). It can be detected using white blood cell count and C-reactive protein (CRP). However, nearly 10% of women have subclinical infection. Laboratory analysis has demonstrated that vaginal interleukin-6 (IL-6) is correlated with PTB. The authors aimed to investigate a bedside test in this context. Method Vaginal secretions were collected from women at high risk of PTB. Samples were incubated for 20 min then analysed by the bedside optical reader (IL-6 in pg/ml). Maternal and neonatal infectious markers and pregnancy outcome were recorded. Results In the 80 women investigated, IL-6 was able to predict PTB, latency to gestation and maternal infection with some efficacy. It was a poor predictor of neonatal infection. Women with visible fetal membranes had significantly higher IL-6 concentrations than those with closed cervices (p=0.002). All of those with visible membranes and a high IL-6 (>50 pg/ml) had a PTB (n=12) compared to half of those with a low IL-6 ( Conclusion IL-6 may be useful in guiding the difficult management of patients with visible membranes and PPROM, for example the potential benefit of a cervical cerclage and antibiotics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".