Usability of an Educational Intervention to Overcome Therapeutic Inertia in Multiple Sclerosis Care
Bibliographic record
Abstract
Background: Educational interventions are needed to overcome knowledge-to-action gaps in clinical care. We previously tested the feasibility and potential efficacy of an educational intervention that facilitates treatment decisions in multiple sclerosis care. A demonstration of the usability of such an intervention is crucial prior to demonstration of efficacy in a large trial. Objectives: To evaluate the usability of a novel, pilot-tested intervention aimed at neurologists to improve therapeutic decisions in multiple sclerosis (MS) care. Methods: We surveyed 50 neurologists from Chile, Argentina, and Canada randomized to an educational intervention arm of a pilot feasibility study using the System Usability Score (SUS) to assess the usability of a traffic light system (TLS)-based educational intervention. The TLS facilitates therapeutic decisions, allowing participants to easily recognize high-risk scenarios requiring treatment escalation. The SUS is a 10 item, validated questionnaire with five response options. The primary outcome was the average and 95% confidence interval (CI) of the SUS score. Values above 68 are considered highly usable. Results: Of 50 neurologists invited to be part of the study, all completed the SUS scale and the study. For the primary outcome, the average usability score was 74.7 (95%CI 70.1-79.2). There was one outlier with a score of 35. The usability score excluding the outlier was 76.8 (95%CI 72.7-80.8). Multivariable analysis revealed no association between participants’ characteristics and the SUS score. Conclusions: Our educational intervention has shown high usability among neurologists. The next step is to evaluate the effectiveness of this educational intervention in facilitating treatment decisions in the management of multiple sclerosis in a large trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".