Canadian treatment optimization recommendations (TOR) as a predictor of disease breakthrough in patients with multiple sclerosis treated with interferon β-1a: analysis of the PRISMS study
Bibliographic record
Abstract
BACKGROUND: Early intervention with an effective disease-modifying drug (DMD) offers the best chance of limiting the inflammatory process that contributes to irreversible axonal damage correlating with disability in multiple sclerosis (MS). It is equally important to ascertain fairly quickly whether patients are responding positively to the choice of therapy to allow time for either a treatment modification or a switch in treatment, a process we termed "treatment optimization". Various treatment optimization recommendations (TOR) have been proposed to help decide when a patient taking an MS DMD might be showing a sub-optimal response. We have applied the clinical scheme proposed by the Canadian TOR to the patients involved in the Prevention of Relapses and disability by Interferon Subcutaneously in MS 4-year (PRISMS-4) study, who received interferon beta-1a treatment for 4 years, with the TOR applied retrospectively at year 1. OBJECTIVE: The aim of this investigation was to examine whether these TOR were able to predict which patients would go on to develop disease breakthrough (defined as any relapses or disease progression), indicative of a sub-optimal response over the ensuing 3 years of study and therefore might have benefited from a change in treatment. RESULTS: We found 39% of patients receiving therapy experienced either a medium or high level of concern of breakthrough after a year of treatment, and 89% of these patients went on to develop further breakthrough over years 2-4. Although 67% of the 61% of patients having no or low-level concern after a year of treatment also experienced further disease breakthrough, it was significantly less than the medium or high group. CONCLUSION: This study shows that the Canadian TOR may be an important tool for early treatment optimization.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".