Cervical insulin‐like growth factor binding protein‐1 (IGFBP‐1) to predict spontaneous onset of labor and induction to delivery interval in post‐term pregnancy
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether insulin-like growth factor binding protein-1 (IGFBP-1) assessed in cervical secretion can predict successful induction and spontaneous onset of labor in post-term pregnancy, compared to ultrasound measurement of cervical length and Bishop score. DESIGN: Cohort study, originating from a randomized controlled trial. SETTING: Obstetric department of a university and tertiary referral hospital, Norway. POPULATION: Five hundred and eight post-term women who had been randomized to induction of labor or expectant management 1 week beyond estimated day of delivery (289 [±2] days of gestation). METHODS: Time to delivery was related to presence of IGFBP-1 in cervical secretion, Bishop score and ultrasound measurement of cervical length recorded at inclusion. MAIN OUTCOME MEASURES: Spontaneous onset of labor and delivery within 3 days in the expectant management, and delivery within 24 hours of induction in the induction group. Test characteristics (sensitivity, specificity and negative and positive values and likelihood ratios) for IGFBP-1, Bishop score and cervical length were calculated. Logistic regression and Cox regression were used to account for parity and body mass index. RESULTS: With expectant management, IGFBP-1 predicted spontaneous labor onset and delivery within 72 hours with low sensitivity and high specificity (0.45 and 0.80, respectively), as did Bishop score (0.24, 0.92). Cervical length was more sensitive (0.67, 0.58). IGFBP-1 predicted successful induction within 24 hours with low sensitivity and high specificity (0.30, 0.85), such as Bishop score (0.06, 1.00) and cervical length (0.45, 0.76). Parity enhanced successful induction. CONCLUSION: IGFBP-1 predicts both spontaneous labor onset and successful induction in post-term pregnancy. Bishop score and cervical length performed equally 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".