The Traffic Light System reduces Therapeutic Inertia in MS Care: results from a RCT (P1.2-096)
Bibliographic record
Abstract
Objective: Our aim was to determine the efficacy the traffic light system (TLS) educational intervention to reduce therapeutic inertia (TI) among neurologists who care for MS patients. Background: TI is commonly observed in the management of cardiovascular diseases and multiple sclerosis (MS). The consequences of TI include poorer clinical outcomes, greater disability and diminished quality of life. There are no proven effective interventions to overcome TI. Design/Methods: In this RCT, 90 neurologists who provide care to MS patients, were randomly assigned to the TLS intervention (n=45) or to the control group (n=45). The TLS educational intervention consisted labelled disease progression risk categories in a short, structured, single session of 5–7 min duration. Participants made therapeutic choices of 10 simulated case-scenarios that assessed TI. The primary outcome was a reduction in TI using a previously established TI score. The proportion of TI among participants was a secondary outcome measure. This trial is registered with ClinicalTrials.gov NCT03134794. Results: All participants completed the study and were included in the primary analysis. The univariate analysis showed lower TI in the TLS group (1.47, 95%CI 1.32–1.61) compared to controls (1.93; 95%CI 1.79–2.08) (p=0.001). Similarly, the TLS group had a lower prevalence of TI compared to controls (0.67, 95%CI 0.62–0.71 vs. 0.82, 95%CI 0.78–0.86; p=0.001). The multivariate analysis, adjusted for age, specialty, years of practice, and risk preference showed a significant reduction in TI for participants randomized to the TLS vs control group (β − 0.68, 95%CI: −1.24; −0.11). The benefits of the TLS was across all TI score categories (Figure). Similarly, for the secondary outcome there was a 70% reduction in TI after the TLS intervention compared to controls (OR 0.30; 95%CI 0.10–0.89). Conclusions: In neurologists who cared for MS patients, a short, simple, and innovative educational intervention reduced TI irrespective of age, expertise, and years of practice. Disclosure: Dr. Saposnik has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen, EMD Serono, Sanofi Genzyme, Novartis Pharmaceuticals,and Roche. Dr. Saposnik has received research support from Roche Boheringher. Dr. Mamdani has nothing to disclose. Dr. Silva has nothing to disclose. Dr. Saladino has nothing to disclose. Dr. Terzaghi has nothing to disclose. Dr. Montalban has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Dr. Montalban received speaking honoraria and travel expenses for scientific meetings, has been a steering committee member of clinical trials or participated in advisory boards of clinical trials in the past 3 years with Biogen, Merck Serono, Genentech, Genzyme, Novartis, Sanofi-Aventis, Teva Pharmaceuticals, Roche, Celgene, Actelion, NMSS and MSIF. Dr. Tobler has nothing to disclose. Dr. Caceres has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".