Neonatal and delivery outcomes in women with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine (1) whether the risk of adverse neonatal and delivery outcomes differs between mothers with and without multiple sclerosis (MS) and (2) whether risk is differentially associated with clinical factors of MS. METHODS: This retrospective cohort study analyzed data from the British Columbia (BC) MS Clinics' database and the BC Perinatal Database Registry. Comparisons were made between births to women with MS (n = 432) and to a frequency-matched sample of women without MS (n = 2,975) from 1998 to 2009. Outcomes included gestational age, birth weight, assisted vaginal delivery, and Caesarean section. Clinical factors examined included age at MS onset, disease duration, and disability. Multivariate regression models adjusting for confounding factors were built for each outcome. RESULTS: Babies born to MS mothers did not have a significantly different mean gestational age or birth weight compared to babies born to mothers without MS. MS was not significantly associated with assisted vaginal delivery (odds ratio [OR], 0.78; 95% confidence interval [CI], 0.50-1.16; p = 0.20) or Caesarean section (OR, 0.94; 95% CI, 0.69-1.28; p = 0.69). There was a slightly elevated risk of adverse delivery outcomes among MS mothers with greater levels of disability, although findings were not statistically significant. Disease duration and age at MS onset were not significantly associated with adverse outcomes. INTERPRETATION: This study provides reassurance to MS patients that maternal MS is generally not associated with adverse neonatal and delivery outcomes. However, the suggestion of an increased risk with greater disability warrants further investigation; these women may require closer monitoring during pregnancy.
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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 | 0.000 | 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 | 0.000 |
| 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".