The Traffic Light System reduces Therapeutic Inertia in MS Care: results from a RCT (P1.2-096)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».