Prioritization of patients for surgery in Canada: The case of hip and knee replacement surgeries in Newfoundland
Bibliographic record
Abstract
Background: Single-entry models (SEM) improve wait times for hip and knee replacement, but little is known whether prioritization implemented in SEM can help meet the benchmarks for consolation/surgery. This study aimed to determine the impact of prioritization on receiving consultation and surgery within the benchmarks. Methods: This is a retrospective cohort study for which two administration databases were linked. Logistic regression was used to investigate the impact of prioritization on receiving consultations and surgery within the benchmarks of 90 and 182 days, respectively, adjusting for patients' characteristics and preference for surgeon. Results: 1,967 patients were included in this study. The odds ratios of having consultation within 90 days for hip replacement patients in priorities 1 and 2 (high priority) were 57.24 (CI: 23.16-141.47) and 14.63 (CI: 6.44-33.25), respectively, compared with those in priority 3. For knee replacement, patients with higher priority were more likely to have consultation within 90 days. Although priority levels were not related to having surgery within 182 days for knee replacement, hip replacement patients with priority 1 (CI: 0.2-0.75) and 2 (CI: 0.16-0.54) were less likely to have surgery within 182 days, compared with those with priority 3. Conclusion: Patients with high priority levels were more likely to have consultation within 90 days for hip and knee replacements. SEM may not help have surgery within 182 days. Prioritization has no impact on receiving surgery within 182 days for knee replacement, but hip replacement patients with high priority were less likely to have surgery within 182 days.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".