Priority Setting in Surgery: Improve theProcess and Share the Learning
Bibliographic record
Abstract
Surgeons and surgical programs encounter priority-setting challenges every day, such as in regard to purchasing new technologies or managing waiting lists for elective surgery. The purpose of this paper was to explore priority setting in surgery. Traditionally in surgery, priority-setting decisions for new technologies have been based on evidence of effectiveness and cost-effectiveness; and decisions about managing waiting lists for elective surgery have been based on urgency rating scores. The fairness of priority-setting processes in surgical programs should be enhanced to permit all relevant information and values to be considered. The quality of these decisions can be improved by using an approach that captures and shares lessons from each priority-setting experience. The approach we propose in this paper- describe, evaluate, and improve using a leading conceptual framework for priority setting, called "accountability for reasonableness"-can be used by any surgical program to improve its priority setting, share lessons with others, and develop an evidence base for how these important health policy decisions should be made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".