Shared medical appointments as a new model for carpal tunnel surgery consultation: A randomized clinical trial
Bibliographic record
Abstract
Background In chronic disease management, shared medical appointments have been shown to improve clinic access, productivity and patient education. However, adoption of this model in surgical consultation is limited, and its effect on surgical patients' satisfaction, comfort and surgical risk recall is unknown. Objective To determine whether shared medical appointments could be applied to carpal tunnel surgery consultation while being equally effective as individual consultation for risk recall, patient comfort and satisfaction. Methods A prospective randomized trial involving 80 patients referred for carpal tunnel release consultation, in which patients were assigned to an educational discussion individually or as part of a shared appointment, was conducted. In a blinded fashion, patients were contacted preoperatively to assess their risk recall and postoperatively to rate their overall satisfaction, comfort and satisfaction with the surgeon. Results Patient demographics were equal. Surgical risk recall was equivalent between shared and individual consults (2.06±1.15 versus 1.64±1.04; P=0.11). More participants in the shared appointments condition remembered the specific risks of infection (61.1% versus 33.3%; P=0.020) and bleeding (30.6% versus 10.3%; P=0.028). There was no difference in overall satisfaction (8.70 versus 8.88; P=0.75), satisfaction with the surgeon (8.05 versus 8.13; P=0.92) or overall comfort (8.80 versus 8.31; P=0.46). Discussion Shared medical appointments for carpal tunnel surgery consultation were equivalent to individual consultation in terms of surgical risk recall, patient satisfaction and comfort. Conclusion These results support the use of shared appointments for large-volume, low-variation surgery.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".