P.024 The influence of disease modifying therapies on short-term disease progression in a cohort of relapsing-remitting multiple sclerosis patients in Newfoundland and Labrador
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system accompanied by chronic inflammation and neurodegeneration. An unmet clinical need in the management of MS is how to select an initial disease modifying therapy (DMT). Real-world evidence suggests that early aggressive control with high-efficacy medications results in better long-term prognosis. Methods: This retrospective study was conducted at Memorial University using Relapsing Remitting MS (RRMS) patients enrolled in the HITMS study. Analysis included study participants aged 18+ with RRMS and three years of clinical visits. Disability progression was measured by the Expanded Disability Status Scale (EDSS) and defined as a change of ≥ 1.0. Study subjects were categorized according to DMT at their initial visit. Results: In this cohort, 87 participants met the inclusion criteria; 67 were stable and 20 had disability progression. There was no significant difference in disability progression based on DMT regimen, and age, sex, and disease duration did not affect disability progression. Conclusions: Despite evidence that all RRMS patients go on a DMT, our cohort demonstrated a significant proportion remain DMT naive. Furthermore, the selection of DMT in this cohort appears to be appropriate, as there were no obvious differences in disability progression regardless of DMT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".