Predicting early epidurals: association of maternal, labor, and neonatal characteristics with epidural analgesia initiation at a cervical dilation of 3 cm or less
Bibliographic record
Abstract
BACKGROUND: Retrospective studies have associated early epidural analgesia with cesarean delivery, but prospective studies do not demonstrate a causal relationship. This suggests that there are other variables associated with early epidural analgesia that increase the risk of cesarean delivery. This study was undertaken to determine the characteristics associated with early epidural analgesia initiation. METHODS: Information about women delivering at 37 weeks or greater gestation with epidural analgesia, who were not scheduled for cesarean delivery, was extracted from the McGill Obstetric and Neonatal Database. Patients were grouped into those who received epidural analgesia at a cervical dilation of ≤3 cm and >3 cm. Univariable and multivariable logistic regression was used to determine the maternal, neonatal, and labor characteristics that increased the risk of inclusion in the early epidural group. RESULTS: Of the 13,119 patients analyzed, multivariable regression demonstrated odds ratios (OR) of 2.568, 5.915 and 10.410 for oxytocin augmentation, induction, and dinoprostone induction of labor (P < 0.001). Increasing parity decreased the odds of early epidural analgesia (OR 0.780, P < 0.001), while spontaneous rupture of membranes (OR 1.490) and rupture of membranes before labor commenced (OR 1.288) were also associated with early epidural analgesia (P < 0.001). Increasing maternal weight (OR 1.049, P = 0.002) and decreasing neonatal weight (OR 0.943, P < 0.001) were associated with increasing risk of early epidural analgesia. CONCLUSION: Labor augmentation and induction, nulliparity, rupture of membranes spontaneously and before labor starts, increasing maternal weight, and decreasing neonatal weight are associated with early epidural analgesia. Many of these variables are also associated with cesarean delivery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".