Exploring fetal fibronectin testing as a predictor of labour onset: In parturient women from isolated communities.
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the fetal fibronectin assay would be useful for determining if a woman was close to a term delivery. If effective, this test would allow parturient women to stay in their communities longer. DESIGN: This feasibility study used a prospective cohort design to examine the negative predictive value of the fetal fibronectin test at term. SETTING: Iqaluit, NU. PARTICIPANTS: A total of 30 parturient women from rural and isolated communities in Nunavut. INTERVENTION: Starting at 36 weeks' gestation, women were tested every 2 days, and after 39 weeks this increased to every day until labour. MAIN OUTCOME MEASURES: The negative predictive value of the fetal fibronectin test was assessed. RESULTS: Women were no more likely to give birth at 7 or more days after their last negative fetal fibronectin test result relative to their likelihood of giving birth at 6 or fewer days after their last negative test result. Hence, the presence of fetal fibronectin in cervical secretion did not predict term delivery. CONCLUSION: This project indicated that the fetal fibronectin test did not have adequate sensitivity or specificity as a diagnostic measure to predict a delay of labour at term.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".