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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".