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Record W3186380202 · doi:10.1177/20552173211032313

The road to conception for women with multiple sclerosis

2021· article· en· W3186380202 on OpenAlexaffabout
A. Dessa Sadovnick, Maria Criscuoli, Irene M. Yee, Robert Carruthers, Alice Schabas, Penelope Smyth

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsPregnancyMultiple sclerosisProspective cohort studyMedicinePsychiatrySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this prospective "real world" study is to gain insight into the different "roads to conception" that women with MS take as part of the prospective Canadian Multiple Sclerosis Pregnancy Study (CANPREG-MS). METHODS: Participants are women with MS who are planning a pregnancy. Data cut-off for analyses was April 30, 2020. RESULTS: We believe this is the first prospective National study of women with MS planning pregnancies.The data are for the first 44 women enrolled of whom 26 achieved pregnancy by cut-off date. Seven women used assisted reproductive technologies (ARTs); 6 stopped disease modifying therapy (DMT) against their neurologists' recommendations; 6 had an interruption(s) in trying to conceive due to MS relapses, MRI-detected inflammation, or limited "windows of opportunity" between DMT courses. CONCLUSION: The study illustrates the roads that women take to conception, even if they are on the same therapy and have similar clinical expression of MS. Advice given by treating neurologists on washout periods show discrepancies. This paper highlights the real problem that there is no definitive, international consensus on managing these women due to the lack of "real world" data and thus the goal of CANPREG-MS is to provide such real world data.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.192
GPT teacher head0.390
Teacher spread0.197 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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