Utility of the Canadian Treatment Optimization Recommendations (TOR) in MS Care
Bibliographic record
Abstract
OBJECTIVES: Criteria for Treatment Optimization Recommendations (TOR) for patients with multiple sclerosis (MS) identify suboptimal response to disease-modifying treatment (DMT). The Canadian TOR (CanTOR) were used to indicate recommendations for treatment switches or treatment maintenance based on relapse, disease progression and magnetic resonance imaging (MRI) criteria in patients. We assessed concordance between the TOR and clinicians' decisions regarding treatment response and identified prevalence of patients with MS receiving DMT meeting medium/high levels of concern according to TOR. METHODS: Prospective baseline and end-of-study assessments of patients with relapsing-remitting MS (RRMS) or clinically isolated syndrome were conducted in this open-label, 12-month, Phase IV, observational Canadian study. RESULTS: Data were reported for 184 patients (female 72%, mean age 39 years) of which 96% had RRMS. The TOR criteria identified 19 (10.3%) patients with suboptimal response to treatment. Twelve patients had ≥1 high level of concern. Two patients had ≥2 medium levels of concern. Concordance between TOR and clinician decision in maintaining treatment was 95.3%. Where treatment change was recommended by the TOR, concordance was 29.4%. Clinicians identified the TOR as the principal reason for changing treatment in 50.0% of cases where the TOR identified suboptimal response. The TOR were considered useful by 70.6% of clinicians when treatment optimization was recommended and by 55.3% when maintaining treatment was recommended. CONCLUSIONS: The TOR criteria can identify suboptimal response in this patient cohort. Concordance between TOR and clinician decision was high when maintaining treatment was recommended. Usefulness of the TOR was most apparent when treatment optimization was recommended.
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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 | 0.027 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".