Bibliographic record
Abstract
In this issue of Neurology ®, Tremlett et al.1 examine whether relapses influence disability progression in multiple sclerosis (MS), and specifically how the influence of relapses on disability progression differs by disease duration. This is important because of the implications of costly disease-modifying therapies and their potential long-term benefits; they clearly reduce relapse frequency but exhibit less impressive benefits on disability in clinical trials of 2 to 3 years’ duration.2 Prior studies of placebo groups from clinical trials suggest that relapses affect the accumulation of disability over short observation periods,3 while other studies using similar populations argued that relapses do not influence disability consistently.4 Investigators examining the Lyon MS cohort suggested that relapses during the first 5 years after disease onset influence the speed of disability accumulation initially, but once a certain level of (irreversible) disability is reached, disability progression is independent of other factors.5 Tremlett et al. used a large population-based clinical database including 2,477 patients with more than 11,000 relapses to examine the effect of relapses throughout the disease course using 2 measures, the time to needing a cane (Expanded Disability Status Scale [EDSS] score = 6) and the time to developing secondary progressive MS (SPMS).1 They included the cumulative number of relapses in their regression model as a time-dependent covariate, a previously unused approach. Having relapses within …
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".