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Record W2935354372 · doi:10.1016/j.msard.2019.04.003

Management strategies for female patients of reproductive potential with multiple sclerosis: An evidence-based review

2019· review· en· W2935354372 on OpenAlexaff
Patricia K. Coyle, Jiwon Oh, Melinda Magyari, Celia Oreja‐Guevara, Maria K. Houtchens

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersSanofiGenzyme
KeywordsMultiple sclerosisMedicinePregnancyDiseasePostpartum periodIntensive care medicineImmunologyPathology

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is an inflammatory, demyelinating, neurodegenerative, immune-mediated disease primarily diagnosed in early adulthood. Multiple sclerosis mostly impacts women of reproductive potential, with pregnancy and birth outcomes being major concerns for many patients. While there is ample evidence that the disease itself has no impact on pregnancy, many women living with MS still question their ability to have children, and the impact of childbearing on their disease in the short and long term. Such questions emphasize the importance of proper guidance from healthcare professionals, particularly neurologists. Management considerations are also complicated by the growing list of available treatment options. This review will summarize current evidence and expert opinion around the management of female MS patients of reproductive potential, from family planning to the postpartum period. Current guidelines on the use of disease-modifying therapies throughout pregnancy will be discussed, as well as other general medical recommendations, to minimize MS disease activity in the peripartum period.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.274
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations60
Published2019
Admission routes1
Has abstractno

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