Herding: a new phenomenon affecting medical decision-making in multiple sclerosis care? Lessons learned from DIScUTIR MS
Bibliographic record
Abstract
PURPOSE: Herding is a phenomenon by which individuals follow the behavior of others rather than deciding independently on the basis of their own private information. A herding-like phenomenon can occur in multiple sclerosis (MS) when a neurologist follows a therapeutic recommendation by a colleague even though it is not supported by best practice clinical guidelines. Limited information is currently available on the role of herding in medical care. The objective of this study was to determine the prevalence (and its associated factors) of herding in the management of MS. METHODS: We conducted a study among neurologists with expertise in MS care throughout Spain. Participants answered questions regarding the management of 20 case scenarios commonly encountered in clinical practice and completed 3 surveys and 4 experimental paradigms based on behavioral economics. The herding experiment consisted of a case scenario of a 40-year-old woman who has been stable for 3 years on subcutaneous interferon and developed a self-limited neurological event. There were no new magnetic resonance imaging (MRI) lesions. Her neurological examination and disability scores were unchanged. She was advised by an MS neurologist to switch from interferon to fingolimod against best practice guidelines. Multivariable logistic regression analysis was conducted to evaluate factors associated with herding. RESULTS: =0.12). In multivariate analyses, the number of MS patients seen per week was positively associated with herding (odds ratio [OR] 1.08, 95% CI 1.01-1.14). Conversely, physician's age, gender, years of practice, setting of practice, or risk preferences were not associated with herding. CONCLUSION: Herding was a common phenomenon affecting nearly 8 out of 10 neurologists caring for MS patients. Herding may affect medical decisions and lead to poorer outcomes in the management of 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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".