Therapeutic Inertia in Multiple Sclerosis Care: A Study of Canadian Neurologists
Bibliographic record
Abstract
Introduction: Limited information is available on the prevalence of therapeutic inertia (TI) and its associated factors in Canada. Objectives: i) To evaluate factors associated with TI amongst neurologists caring for MS patients across Canada; ii) to compare TI between Canadian neurologists with historical controls from prior studies. Design: 108 neurologists with expertise in MS were invited to participate in an online study in Canada. Participants answered questions regarding their clinical practice, risk preferences, management of 10 simulated case-scenarios. TI was defined as lack of treatment initiation or escalation when there was clear evidence of clinical and radiological disease activity (8 case-scenarios, 440 individual responses). TI scores observed in the Canadian study were compared with those observed in Argentina and Chile, as both studies followed the same design, case-scenarios and methodologies. Results: 55 Canadian neurologists completed the study (completion rate: 50.9%). Overall, 54 of 440 (12.3%) individual responses were classified as TI. 60% of participants displayed TI in at least one case-scenario. The mean TI score across Canada [0.98 (SD=1.15)] was significantly lower than the TI score observed in the Argentinean-Chilean [1.82 (SD=1.47); p<0.001] study. Conclusions: TI in Canada was observed in 6 out of 10 neurologists, affecting on average 1 in 8 therapeutic decisions in MS care. TI in Canada is significantly lower than in the other studied countries. Factors associated with TI include older age, lower years of experience and willingness to risk disease progression by avoiding treatment initiation or treatment change.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".