A contemporary profile of primary progressive multiple sclerosis participants from the NARCOMS Registry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".