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Record W2800532467 · doi:10.1177/1753495x18758868

Devic syndrome and pregnancy: A case series

2018· article· en· W2800532467 on OpenAlexaffabout
Danielle Wuebbolt, Vanessa Nguyen, Rohan D’Souza, Ahraaz Wyne

Bibliographic record

VenueObstetric Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHamilton Health SciencesMcMaster UniversityUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineNeuromyelitis opticaPregnancyTransverse myelitisOptic neuritisPrednisonePediatricsPostpartum periodObstetricsMyelitisMultiple sclerosisSurgeryImmunologySpinal cord

Abstract

fetched live from OpenAlex

BACKGROUND: Devic syndrome or neuromyelitis optica is an autoimmune neurological condition characterized by relapsing symptoms of optic neuritis and transverse myelitis. Women with neuromyelitis optica suffer from adverse pregnancy outcomes and high relapse rates during pregnancy and the postpartum period. METHODS: This case series describes 13 pregnancies in four women with neuromyelitis optica managed at a tertiary hospital in Toronto, Canada. RESULTS: In most cases, neurologic symptoms either worsened or developed for the first time during pregnancy or the postpartum period, and often responded to a combination of steroids, immunosuppressant medications, plasma exchange and intravenous immunoglobulin. The 13 pregnancies resulted in two miscarriages, three preterm and eight term births. One fetus whose mother was on gabapentin, prednisone and spironolactone, had congenital malformations (aplastic lung and fused fingers). CONCLUSIONS: Despite high frequency of relapses in pregnancy and the postpartum period, with multidisciplinary team management, outcomes for women with neuromyelitis optica are encouraging.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.333
Teacher spread0.269 · 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 designCase report
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

Citations9
Published2018
Admission routes2
Has abstractyes

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