Evidence map›Paper›PMID 28871238›Full record

ArticleFrontiers in neurology2017

Overcoming Therapeutic Inertia in Multiple Sclerosis Care: A Pilot Randomized Trial Applying the Traffic Light System in Medical Education.

Gustavo Saposnik, Jorge Maurino, Angel P Sempere, Maria A Terzaghi, Christian C Ruff, Muhammad Mamdani, Philippe N Tobler, Xavier Montalban

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03134794 (Decision Making Under Uncertainty in MS Care), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
8.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03134794 nacompletednot on this map

Decision Making Under Uncertainty in MS Care: an Innovative Educational Intervention Applying Concepts From Neuroeconomics

TypeinterventionalSponsorUnity Health TorontoRan2017 to 2018Enrolled90ConditionsMultiple SclerosisArmsEducational intervention applying the traffic light system (TLS)
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 6 institutions in 3 countries.

Gustavo SaposnikDivision of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
Jorge MaurinoNeuroscience Area, Medical Department, Roche Farma, Madrid, Spain.
Angel P SempereDepartment of Neurology, Hospital General Universitario de Alicante, Alicante, Spain.
Maria A TerzaghiDivision of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
Christian C RuffLaboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
Muhammad MamdaniHealthcare Analytics Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
Philippe N ToblerLaboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
Xavier MontalbanDivision of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
University of Toronto · CAUniversity of Zurich · CHHospital General Universitario de Alicante Doctor Balmis · ESLaboratory for Social and Neural Systems Research · CHRoche (Spain) · ESSt. Michael's Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysicians 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.

objectivesTo evaluate the feasibility and efficacy of an educational intervention compared to usual care among practicing neurologists caring for patients with multiple sclerosis (MS).

methodsWe 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.

resultsOf 30 neurologists invited to be part of the study, the participation rate was 83.3% (

conclusionAn 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 registrationwww.ClinicalTrials.gov, identifier NCT03134794.

Indexed as

decision makingdisease-modifying therapymultiple sclerosisneuroeconomicsrisk aversion

Identifiers

PMID28871238
PMCPMC5567059
OpenAlexW2746425091

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.