Estimating Typical Multiple Sclerosis Disability Progression Speed from Clinical Observations
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is a chronic disease of the central nervous system. Estimates of MS natural history (NH) disability progression speed from clinical observations vary worldwide. This may reflect, in part, variance in censoring-bias) (missing observations) and assumptions about when irreversible disability progression events occurred. We test whether estimates of progression speed which assume midpoint survival time at irreversible disability endpoints are significantly faster than estimates which assume maximum survival time, and are more stable across study groups and time periods. METHODS: Our Nova Scotia NH study population includes 2,240 definite relapsing-onset multiple sclerosis (R-MS) natural history patients with 18,078 Expanded Disability Status Scale (EDSS) clinical observations in study period 1979-2010. Progression speed is measured by rate-of-change in range EDSS 0-6 and by survival time at irreversible endpoints EDSS 1-9. Midpoint censoring-bias-reduction methods are applied to clinical observations. FINDINGS: Typical EDSS increase per year in range EDSS 0-6, assuming midpoint survival time, is estimated to be 0.168 for all R-MS, 0.204 for eventually-DMD-treated patients and 0.155 for never-DMD-treated patients. Estimates assuming midpoint rather than maximum survival time are significantly faster: 16% faster for all R-MS natural history patients, 6% faster for eventually-DMD-treated patients, and 21% faster for never-DMD-treated patients. The variability of estimates across study groups and time periods decreased when midpoint survival time was assumed. CONCLUSIONS: Estimates of typical disease progression speed from 1979-2010 Nova Scotia clinical observations are sensitive to censoring-bias and to analysts' survival time assumptions. Censoring-bias-adjusted estimates of typical natural history disability progression speed in relapsing-onset multiple sclerosis patients are significantly faster, and less variable within and across study groups and time periods, than unadjusted estimates, and are, arguably, more relevant for various stakeholders. The application of censoring-bias-reduction methods to other multiple sclerosis clinical databases may reduce variability in estimates of disability progression speed worldwide.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".