The Manitoba arthroplasty waiting list: impact on health‐related quality of life and initiatives to remedy the problem
Bibliographic record
Abstract
RATIONALE: With the aging population, arthritis of the hip and knee is increasing exponentially. While total joint replacement of the hip and knee have been proven to provide excellent outcomes for this debilitating clinical entity, the demand in Canada has grown to such an extent that there are thousands of people suffering on wait lists across the country. There have been numerous recent studies focusing on the effects that waiting have on patient post-operative subjective and objective clinical outcomes. AIMS AND OBJECTIVES: This commentary attempts to provide a review of the relevant data on the impact that waiting has on the health related quality of life of these patients. Additionally, the hip and knee arthroplasty wait list in Manitoba is illustrated to provide an example of interventions that have helped to combat a potential crisis situation in the province. METHODS: The literature on the impact of waiting times on health-related quality of life in hip and knee arthroplasty patients is reviewed. The example of the Manitoba arthroplasty waiting list is then provided to illustrate potential measures that can be implemented to decrease waiting times. RESULTS: The literature shows that health-related quality of life declines as patients wait for surgery. The interventions that the province of Manitoba has implemented in the last seven years seem to be reducing waiting times and the number of patients on the waiting list. CONCLUSIONS: The framework used in Manitoba may serve as an example for other provinces and potentially countries that find themselves faced with the same problem. However, more measures are required to build on the positive results encountered with these initial successes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".