Gaps and discordance in assessment of PIRA identified between HCPs and people with MS: Results of international surveys
Bibliographic record
Abstract
OBJECTIVES: Progression independent of relapse activity (PIRA) is a major contributor to long-term disability accumulation in multiple sclerosis (MS). This study aimed to explore the assessment of PIRA in clinical practice from the perspectives of people living with MS (pwMS) and healthcare providers (HCPs). METHODS: Cross-sectional surveys were conducted among 310 pwMS and 360 HCPs involved in MS care across seven countries in North America and Europe. RESULTS: HCPs proved to be more motor-focused, primarily through neurological examination and EDSS (75 %), whereas pwMS reported fatigue as the domain most affected (67 %), which was the least assessed domain by HCPs (31 %). 54-61 % of pwMS indicated that the thoroughness, average time spent, and frequency of PIRA assessment had remained the same since diagnosis. As reported by HCPs, roughly 40 % of PIRA assessment remained the same in pwMS with even moderate-severe disability, likely due to time constraints, considered the most limiting factor to measuring PIRA, as well as the lack of a comprehensive, standardized approach and sensitive tools to measure disability as reported by HCPs accurately. CONCLUSION: MS care necessitates a standardized and time-sensitive approach for assessing disability in the absence of relapse, to optimize care and enhance routine disability assessment and monitoring.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".