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Record W2788702341 · doi:10.1212/wnl.0000000000005075

Maternal and fetal risks of natalizumab exposure in utero

2018· letter· en· W2788702341 on OpenAlexaff
Ruth Ann Marrie

Bibliographic record

VenueNeurology · 2018
Typeletter
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsUniversity of ManitobaManitoba Health
Fundersnot available
KeywordsNatalizumabMedicinePregnancyMultiple sclerosisDiseaseFetusIn uteroAdverse effectObstetricsYoung adultPediatricsInternal medicineImmunology

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) typically presents in young adulthood, often at a time when decisions about reproduction have not been made yet. Although some individuals with MS will choose not to have children for MS-related reasons,1 many others will choose to do so. For women, this raises particular concerns. Women with MS are slightly more likely to deliver infants who are small for gestational age, but maternal MS is not associated with a greater risk of birth defects.2 During the third trimester of pregnancy, annualized relapse rates decrease, followed by an increased relapse risk particularly in the first 3 months of the postpartum period.3 Consistent with this observation, new, enlarging, or gadolinium-enhancing lesions are often detected postpartum.4 Disease activity gradually returns to prepartum levels, and no long-term adverse effects of pregnancy on disease progression have been identified.5 In the modern therapeutic era, women with MS and their physicians face sometimes challenging decisions regarding disease-modifying therapy (DMT) and how this will affect maternal and fetal health. Although use of injectable DMTs prepartum does not appear to alter the aforementioned pattern of disease activity in the third trimester or postpartum,6 this may not be the case for more effective therapies, such as natalizumab, which are associated with rebound disease activity when they are withdrawn.7

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.028
GPT teacher head0.264
Teacher spread0.235 · 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 designNot applicable
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

Citations2
Published2018
Admission routes1
Has abstractyes

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