Regret and Therapeutic Decisions in Multiple Sclerosis Care: Literature Review and Research Protocol
Bibliographic record
Abstract
Background:Decisions based on erroneous assessments may result in unrealistic patient and family expectations, suboptimal advice, incorrect treatment, or costly medical errors. Regret is a common emotion in daily life that involves counterfactual thinking when considering alternative choices. Limited information is available on care-related regret affecting healthcare professionals managing patients with multiple sclerosis (MS). Methods:We reviewed identified gaps in the literature by searching for the combination of the following keywords in Pubmed: “regret and decision,” “regret and physicians,” and “regret and nurses.” An expert panel of neurologists, a nurse, a psychiatrist, a pharmacist, and a psychometrics specialist participated in the study design. Care-related regret will be assessed by a behavioral battery including the standardized questionnaire Regret Intensity Scale (RIS-10) and 15 new specific items. Six items will evaluate regret in the most common social domains affecting individuals (financial, driving, sports—recreation, work, own health, and confidence in people). Another nine items will explore past and recent regret experiences in common situations experienced by healthcare professionals caring for patients with MS. We will also assess concomitant behavioral characteristics of healthcare professionals that could be associated with regret: coping strategies, life satisfaction, mood, positive social behaviors, occupational burnout, and tolerance to uncertainty. Planned Outcomes:This is the first comprehensive and standardized protocol to assess care-related regret and associated behavioral factors among healthcare professionals managing MS. These results will allow to understand and ameliorate regret in healthcare professionals. Spanish National Register(SL42129-20/598-E).
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".