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Record W4317726029 · doi:10.1177/13524585221146270

The Canadian Multiple Sclerosis Pregnancy Study: First-trimester miscarriages in women with multiple sclerosis

2023· article· en· W4317726029 on OpenAlexaffabout
A. Dessa Sadovnick, Maria Criscuoli, Irene M. Yee, Robert Carruthers, Alice Schabas, Virginia Devonshire, Penelope Smyth

Bibliographic record

VenueMultiple Sclerosis Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMultiple sclerosisMiscarriagePregnancyMedicineObstetricsFirst trimesterPopulationGynecologyAbortionGestationPsychiatryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: There is increasing need for evidence-based data on reproduction for women with multiple sclerosis (MS). First-trimester (first 13 weeks) miscarriages are relatively common in the general population. It is therefore important to have information on the frequency with which this occurs in women with MS. METHODS: The Canadian Multiple Sclerosis Pregnancy Study (CANPREG-MS) is a prospective study on women with MS who are pregnant or actively trying to conceive. As far as we are aware, this is the first study on miscarriages for this population that takes into account each woman's entire pregnancy history (i.e. before and after the MS diagnosis as well as during enrollment in CANPREG-MS). RESULTS: There were 208 pregnancies during the study and 36 resulted in first-trimester miscarriage for a rate of 17.31%, within the expected range of 15%-20% for the general population. CONCLUSIONS: CANPREG-MS provides real world data that there does not appear to be an increase in first-trimester miscarriages for women with MS. This information will be helpful to women with MS and their 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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.003
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 head0.117
GPT teacher head0.289
Teacher spread0.172 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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