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Development of Chronic Black Holes (CBH) Predicts Long Term Disability: Post-hoc Analysis of Magnetic Resonance Imaging (MRI) Data in the PRISMS Study (P7.251)

2015· article· en· W1805414635 on OpenAlexaff
Tony Traboulsee, David Li, Juanzhi Fang, Fernando Dangond, Ludwig Kappos

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British Columbia HospitalVancouver Coastal Health Research Institute
Fundersnot available
KeywordsMagnetic resonance imagingPost-hoc analysisPost hocTerm (time)MedicineNuclear magnetic resonancePhysicsInternal medicineRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE:Assess predictive value of CBH on long-term clinical outcomes in terms of disability in patients with relapsing-remitting multiple sclerosis (MS). BACKGROUND:CBH indicate irreversible axonal loss in MS, and may be associated with long-term disability progression. DESIGN/METHODS:Patients in PRISMS were randomized to IFN β-1a, 44 or 22 μg three times weekly or placebo; after 2 years, placebo patients were re-randomized to IFN β-1a 44 or 22 μg (delayed treatment [DT]). This was a retrospective analysis of MRI scans in patients with monthly scans from Months -1 to 9. New enhancing lesions (NEL) were evaluated from Months -1 to 3; CBH evolving from NEL were assessed at Month 8/9. Association between CBH and disability outcome in follow-up to 4 years was analyzed according to treatment group. RESULTS:196 patients were included (IFN β-1a combined, n=129; DT, n=67). In the IFN β-1a group, 73/129 had NEL versus 49/67 in the DT group from Months -1 to 3; 蠅1 CBH at Month 8/9 developed in 25/73 (34[percnt]) and 27/49 (55[percnt]) patients with 蠅1 NEL in the IFN β-1a and DT groups, respectively. CBH were associated with disability progression versus no CBH (confirmed 3-month Expanded Disability Status Scale [EDSS] progression rate at 4 years 55.8[percnt] versus 43.1[percnt]). In patients with 蠅1 CBH at Month 8/9, proportions with confirmed EDSS progression at 1-, 2-, 3- and 4-years were: DT 37.0[percnt], 48.1[percnt], 55.6[percnt], 59.3[percnt], respectively; IFN β-1a 12.0[percnt], 32.0[percnt], 52.0[percnt], 52.0[percnt], respectively. In patients with 蠅1 CBH, median EDSS score in the DT group increased from 2.00 at Month 8/9 to 3.5 at 4 years; in the IFN β-1a group, the score increased from 2.5 to 3.0. CONCLUSIONS:CBH are associated with disability progression. IFN β-1a led to fewer NEL evolving into CBH, and slower disability progression at 4 years. Study Supported by: Merck Serono.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.350
Teacher spread0.271 · 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 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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