Prioritizing patients for elective surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Priority scoring tools are moot as means for dealing with burgeoning elective surgical waiting lists. There is ongoing development work in New Zealand, Canada and the UK. This emerging international perspective is invaluable in determining the application of these tools and addressing any pitfalls. METHODS: A systematic electronic literature review was performed. Information was also retrieved using a search of reference lists of all papers included in the review and contact with those who were involved in the development of such criteria. RESULTS: The ethical basis of prioritization differed among priority scoring tools and in a number was not stated. The majority of tools covered criteria for specific procedures. Delphi consensus methods and regression were the predominant methods for -deter-mining -specific criteria. Authors' opinions were the main source of generic criteria. Linear and non-linear models or matrices sum-mated criteria. CONCLUSION: There is debate over the ethical basis for prioritization. It is a concern that it is not addressed in many studies. The development of generic criteria showed a dearth of consensus approaches that represents a significant gap in our knowledge. On the aspects of summation and weighting, the impact of assumptions on the prioritization of patients may not have been fully explored.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".