Overcoming Therapeutic Inertia in Multiple Sclerosis Care: A Pilot Randomized Trial Applying the Traffic Light System in Medical Education
Bibliographic record
Abstract
BACKGROUND: Physicians often do not initiate or intensify treatments when clearly warranted, a phenomenon known as therapeutic inertia (TI). Limited information is available on educational interventions to ameliorate knowledge-to-action gaps in TI. OBJECTIVES: To evaluate the feasibility and efficacy of an educational intervention compared to usual care among practicing neurologists caring for patients with multiple sclerosis (MS). METHODS: We conducted a pilot double-blind, parallel-group, randomized clinical trial. Inclusion criteria included neurologists who are actively involved in managing MS patients. Participants were exposed to 20 simulated case-scenarios (10 cases at baseline, and 10 cases post-randomization to usual care vs. educational intervention) of relapsing-remitting MS with moderate or high risk of disease progression. The educational intervention employed a traffic light system (TLS) to facilitate decisions, allowing participants to easily recognize high-risk scenarios requiring treatment escalation. We also measured differences between blocks to invoke decision fatigue. The control group responded as they would do in their usual clinical practice not exposed to the educational intervention. The primary feasibility outcome was the proportion of participants who completed the study and the proportion of participants who correctly identified a high-risk case-scenario with the "red traffic light." Secondary outcomes included decision fatigue (defined as an increment of TI in the second block of case-scenarios compared to the first block) and the efficacy of the educational intervention measured as a reduction in TI for MS treatment. RESULTS: = 0.14). For the efficacy outcome, we found a non-significant reduction in TI for the targeted intervention compared to controls (22.6 vs. 33.9% post-intervention; OR 0.57; 95% CI 0.26-1.22). CONCLUSION: An educational intervention applying the TLS is feasible and shows some promising results in the identification of high-risk scenarios to reduce decision fatigue and TI. Larger studies are needed to determine the efficacy of the proposed educational intervention. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT03134794.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".