Prognostic factors for long-term outcomes in relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
Objective The objective of this article is to investigate potential clinical and MRI predictors of long-term outcomes in multiple sclerosis (MS). Methods This was a post hoc analysis using data from all 382 patients in the PRISMS long-term follow-up (LTFU) study collected up to eight years after randomisation. An additional analysis was performed including only those patients originally randomised to receive early subcutaneous interferon (IFN) β-1a ( n = 259). Baseline/prestudy variables, indicators of early clinical and MRI activity (baseline to month 24), and indicators of IFN β-1a treatment exposure (including medication possession ratio (MPR)) were investigated as candidate prognostic factors for outcomes measured from baseline and from month 24 to LTFU. Explanatory variables identified from univariate regression models ( p ≤ 0.15) were selected for inclusion in stepwise multiple regression models. Results Candidate prognostic factors selected by the univariate analysis ( p ≤ 0.15) included age, MS duration, baseline brain volume, EDSS score, and log(T2 burden of disease (BOD)). In most of the multivariate regression models applied, higher baseline brain volume and MPR predicted better long-term clinical outcomes, while higher baseline and greater early increase in EDSS score predicted worse outcomes. Conclusion Identification of markers that may be prognostic for long-term disability could help identify MS patients at higher risk of disability progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".