Decision-making in Multiple Sclerosis: The Role of Aversion to Ambiguity for Therapeutic Inertia among Neurologists (DIScUTIR MS)
Bibliographic record
Abstract
Background: Therapeutic inertia (TI) in multiple sclerosis (MS) is defined as the lack of treatment escalation when there is evidence of disease activity. Limited information is available on physician-related factors influencing TI in MS. The aim of the study was to evaluate whether physicians’ risk preferences are associated with TI in the management of MS by applying concepts from behavioral economics. Methods: A study with neurologists managing patients with MS was conducted in Spain. Participants answered questions regarding the management of 20 case-scenarios and completed three surveys and four experimental paradigms based on behavioral economics. Surveys and experiments included standardized tests to measure aversion to risk and ambiguity, physicians’ reactions to uncertainty, and questions related to risk preferences in different domains. Results: Of 161 neurologists who were invited, 136 agreed to participate, and 96 completed the survey (response rate: 60%). TI was present in 68.8% of participants. Total aversion to ambiguity and low tolerance to uncertainty were observed in 22.9% and 42.7% of participants, respectively. Aversion to ambiguity was associated with a higher prevalence of TI (86.4% with aversion to ambiguity vs. 63.5% without aversion to ambiguity; p=0.042). In multivariate analyses, aversion to ambiguity was the strongest predictor of TI (OR 7.39; 95%CI 1.40-38.9), followed by low tolerance to uncertainty (OR 3.47; 95%CI 1.18-10.2). TI was less common among neurologists with greater volumes of patients per week as well as among MS specialists. Conclusion: Therapeutic inertia is a common phenomenon affecting nearly 7 out of 10 neurologists caring for MS patients. Behavioral economics is an innovative approach that may help our understanding of decision making in MS.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".