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Record W2614325279 · doi:10.1177/1352458517711274

A contemporary profile of primary progressive multiple sclerosis participants from the NARCOMS Registry

2017· article· en· W2614325279 on OpenAlexaff
Amber Salter, Nina Thomas, Tuula Tyry, Gary Cutter, Ruth Ann Marrie

Bibliographic record

VenueMultiple Sclerosis Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineMultiple sclerosisCohortPopulationCohort studyClinical trialPhysical therapyInternal medicinePediatricsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Primary progressive multiple sclerosis (PPMS) represents 10%-15% of all multiple sclerosis (MS) diagnoses. Information regarding socio-demographic and clinical characteristics of persons with PPMS is limited. OBJECTIVE: To characterize persons with PPMS in the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. METHODS: We compared demographic and health-related characteristics of NARCOMS Registry participants reporting PPMS in the spring 2015 update survey with those reporting relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), with characteristics of published PPMS cohorts. RESULTS: Of 8004 responders, 6774 self-reported a clinical course of PPMS, SPMS, or RRMS. The PPMS cohort ( n = 632, 9.3%) reported a mean (standard deviation (SD)) age of 64.3 (8.9) years; 62.7% were female; the SPMS and RRMS cohorts were younger and had a higher proportion of females. The NARCOMS PPMS cohort differed in age, time from onset and diagnosis, and proportion of females compared to population-based and clinical trial cohorts. Median (25%, 75%) number of comorbidities was 2 (1, 2) for each cohort with vascular comorbidities being most frequently reported. CONCLUSION: The NARCOMS population provides a different perspective on persons with PPMS than clinical trials. A better understanding of the characteristics of persons with PPMS may help address unmet needs in this population.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.296
GPT teacher head0.344
Teacher spread0.048 · 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 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

Citations16
Published2017
Admission routes1
Has abstractyes

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