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Record W4403215541 · doi:10.1177/20552173241285546

Cesarian sections in women with multiple sclerosis: A Canadian prospective pregnancy study

2024· article· en· W4403215541 on OpenAlexafffundabout
A. Dessa Sadovnick, Maria Criscuoli, Irene M. Yee, Robert Carruthers, Virginia Devonshire, Penelope Smyth, Kristen M. Krysko

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of TorontoSt. Michael's HospitalUniversity of AlbertaUniversity of British Columbia
FundersMultiple Sclerosis Society of CanadaBiogen
KeywordsPregnancyMultiple sclerosisObstetricsProspective cohort studyMedicinePsychiatrySurgeryBiology

Abstract

fetched live from OpenAlex

Background: An increasing number of women with multiple sclerosis (wMS) are considering pregnancy. Prior studies suggest increased rate of elective cesarian sections (C-sections) in wMS. Methods: The Canadian Multiple Sclerosis Pregnancy Study (CANPREG-MS) is a prospective study on pregnant wMS. This report shows comparisons between (i) CANPREG-MS wMS delivered by C-section and the general population and (ii) C-section and vaginal deliveries in this study cohort. Results: = .0085). The majority (66.7%) of C-sections were not planned, and typically were performed for obstetrical indications. C-sections were performed at an earlier gestational age than vaginal deliveries, although birthweight did not differ by mode of delivery in wMS. MS relapses (3.2%) and pseudo-relapses (3.2%) were rare in the first month after C-section deliveries, regardless of disease modifying therapy decisions during gestation and postpartum. Conclusions: C-sections were more common in wMS than the general population, but few were because of maternal MS. CANPREG-MS provides informative data for pregnancies in wMS with well-managed and relatively mild disease. This information is helpful to obstetrical and MS healthcare providers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.364
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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