Relationship between early clinical characteristics and long term disability outcomes: 16 year cohort study (follow-up) of the pivotal interferon β-1b trial in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Evaluating the long term benefit of therapy in multiple sclerosis (MS) is challenging. Although randomised controlled trials (RCTs) demonstrate therapeutic benefits on short term outcomes, the relationship between these outcomes and late disability is not established. METHODS: In a patient cohort from the pivotal interferon β-1b trial, the value of clinical and MRI measures were analysed, both at baseline and during the RCT, for predicting long term physical and cognitive outcome. RESULTS: Baseline disability correlated with both physical (R(2)=0.22; p<0.0001) and cognitive (R(2)=0.12; p<0.0001) outcome after 16 years. Accrual of disability during the RCT (R(2)=0.12; p<0.0001) and annualised relapse rates during the trial correlated with physical outcome (R(2)=0.12; p<0.0001) but not with cognition. In contrast, baseline MRI measures of atrophy and lesion burden correlated with cognitive (R(2)=0.21; p<0.0001), but not with physical, outcome. Accumulation of plaque burden measured by MRI did not correlate with late physical disability or with cognitive outcome. Multivariate regression analysis using stepwise elimination demonstrated that baseline variables contributed independently to predicting long term outcomes while trial outcome variables contributed little. Overall, and considerably dependent on baseline measures, the models developed by this method accounted for approximately half of the variance in long term cognitive and disability outcome. CONCLUSIONS: Although on-trial change in some short term clinical measures correlated with long term physical and disability outcomes, the proportion of the variance explained by single commonly employed on-study variables was often small or undetectable. Better correlations were observed for several baseline measures, suggesting that long term outcome in MS may be largely determined early in the disease course. Trial registration number http://Clinical Trials.gov, study registration NCT00206635.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".