Participation in medical decision-making: attitudes of Italians with multiple sclerosis.
Bibliographic record
Abstract
BACKGROUND: Patient involvement in decisions regarding their care has been advocated, but preferences have not been adequately canvassed, particularly in people with multiple sclerosis (MS). OBJECTIVES: To cross-culturally adapt and validate the Italian version of the Control Preference Scale (CPS) subsequently used to assess preferences of people with MS. METHODS: Translation-adaptation into Italian of CPS from the original Canadian English followed by administration in 140 people with MS from five Italian centers (with re-administration in 35) and semi-structured interview. RESULTS: Cross-cultural adaptation of CPS was successful. The 140 people with MS, who varied in clinical and general characteristics, considered the CPS clear and acceptable. Test-retest reliability was moderate (weighted Kappa 0.65; p<0.001). A collaborative role was preferred (61%), followed by passive (33%) and active (6%) roles. Education (odds ratio [OR] 2.43, 95% confidence limits [CI] 1.05-5.66) and length of follow-up at referral center (OR 0.36, 95% CI 0.14-0.92) were associated with choice of an active/collaborative role in the logistic model. CONCLUSIONS: The Italian CPS was well accepted by our MS population. Our data indicate that a high proportion of Italians with MS prefer a more passive role and this should be considered during the clinical encounter.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".