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Record W2472793648 · doi:10.1177/1352458516655478

Trajectory of MS disease course for men and women over three eras

2016· article· en· W2472793648 on OpenAlexaffabout
Stanley Hum, Yves Lapierre, Susan C. Scott, Pierre Duquette, Nancy E. Mayo

Bibliographic record

VenueMultiple Sclerosis Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill University Health CentreCentre Hospitalier de l’Université de MontréalMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMultiple sclerosisCourse (navigation)DiseaseMedicinePhysical medicine and rehabilitationPsychologyGerontologyInternal medicinePsychiatryPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Heterogeneity in disease course exists within multiple sclerosis (MS) subtypes. OBJECTIVE: The objective was to estimate disease course heterogeneity over three distinct onset periods (pre-1995, 1995-2004, and 2005-present) for men and women. METHODS: Group-based trajectory model (GBTM) was used to estimate clusters of patients following stable or unstable disease progression trajectories based on the Expanded Disability Status Scale (EDSS). Inception cohorts were generated from the Montreal Neurological Institute MS Clinic registry. Stable trajectories were defined as an EDSS ⩽3.0 and change ⩽1 point over the study period. Annualized relapse rate (ARR) based on the first 5 years of disease was an explanatory variable. RESULTS: Proportion of women classified as stable was 0% for pre-1995, 69.0% for 1995-2004, and 83.9% post-2005; for men, these proportions were 18.4%, 41.4%, and 53.8%, respectively. Men had lower percentage of stable disease than women in both post-1995 cohorts (chi-square p < 0.0001). ARR was associated with higher disability trajectories in both post-1995 cohort (odds ratios >1.0) but not in the pre-1995 cohort. CONCLUSION: Large proportions of patients remain stable at their initial disability level for at least 15 years. Higher ARR increases the odds of patients being in a higher disability trajectory in the latter cohorts.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.334
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.308
Teacher spread0.228 · 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.

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".

Quick stats

Citations21
Published2016
Admission routes2
Has abstractyes

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