Assessment of Decisional Conflict about the Treatment of carpal tunnel syndrome, Comparing Patients and Physicians
Bibliographic record
Abstract
BACKGROUND: As part of the process of developing a decision aid for carpal tunnel syndrome (CTS) according to the Ottawa Decision Support Framework, we were interested in the level of 'decisional conflict' of hand surgeons and patients with CTS. This study addresses the null hypothesis that there is no difference between surgeon and patient decisional conflict with respect to test and treatment options for CTS. Secondary analyses assess the impact of patient and physician demographics and the strength of the patient-physician relationship on decisional conflict. METHODS: One-hundred-twenty-three observers of the Science of Variation Group (SOVG) and 84 patients with carpal tunnel syndrome completed a survey regarding the Decisional Conflict Scale. Patients also filled out the Pain Self-efficacy Questionnaire (PSEQ) and the Patient Doctor Relationship Questionnaire (PDRQ-9). RESULTS: On average, patients had significantly greater decision conflict and scored higher on most subscales of the decisional conflict scale than hand surgeons. Factors associated with greater decision conflict were specific hand surgeon, less self-efficacy (confidence that one can achieve one's goals in spite of pain), and higher PDRQ (relationship between patient and doctor). Surgeons from Europe have--on average--significantly more decision conflict than surgeons in the United States of America. CONCLUSIONS: Patients with CTS have more decision conflict than hand surgeons. Decision aids might help narrow this gap in decisional conflict.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".