Long wait times for knee and hip total joint replacement in Canada: An isolated health system problem, or a symptom of a larger problem?
Bibliographic record
Abstract
Introduction: The wait times crisis for hip and knee total joint replacement surgery has been a significant health care issue in Alberta and across Canada. Significant resource and financial efforts have been put forward to reduce wait times for surgery as a means of treating patients with osteoarthritis (OA), but the gains achieved were not sustained. Objective: To effectively address wait time issues, an alternative perspective on this problem is presented - that the wait times are an immediate problem for those needing surgery, but are also a symptom of the bigger issue of an inability of health care systems in Canada to address the needs of individuals with early OA with first-line treatment protocols. Discussion: In considering this more comprehensive understanding of the overall OA management problem, encapsulated by the concept of an "osteoarthritis funnel", we outline potential approaches for a solution on a systemic level that integrates services delivery, health care resource allocation and conceptualization of OA in research activities. It also emphasizes the need for a more effective and relevant program of research to address this complex problem that requires unique solutions. Conclusions: New approaches and understanding are needed to address integrated implementation of effective first-line treatments for newly diagnosed osteoarthritis to prevent the expanding demand for joint replacement surgery. While the focus here is on the Canadian perspective, the need to develop and implement better first-line treatments for those with early OA and those at risk for development of OA is not unique to Canada.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".