Labor induction and augmentation in women with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Fatigue and pelvic organ dysfunction are common among women with multiple sclerosis (MS), which may prolong labor and increase the risk of labor induction and/or augmentation. OBJECTIVE: We set out to investigate the association between MS and related clinical factors (disease duration and the Expanded Disability Status Scale, EDSS) with labor induction/augmentation. METHODS: Data from the British Columbia (BC) MS database were linked with the BC Perinatal Database Registry. Multivariable models were used to compare the likelihood of labor induction and augmentation between attempted vaginal deliveries (1998-2009) in women with MS (n=381) and the general population (n=2615). RESULTS: In the MS cohort, 94/381 deliveries (25%) required labor induction and 147/381 deliveries (39%) required labor augmentation. Having MS was not associated with labor induction (adjusted odds ratio (OR)=0.91; 95% confidence interval (CI)=0.68-1.22, p=0.54) or augmentation (adjusted OR=0.91; 95% CI=0.72-1.15, p=0.43), but was associated with multiple methods of labor induction (OR=1.94; 95% CI=1.23-3.06, p=0.004). A higher EDSS score was associated with an increased risk of labor induction (adjusted p=0.04), but not labor augmentation (adjusted p > 0.5). Disease duration was not associated with either outcome (adjusted p > 0.2). CONCLUSIONS: Greater intervention may be required to initiate labor for women with a higher degree of disability due to MS.
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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 | 0.000 | 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) | 0.003 | 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".