``Not Everyone Who Needs One Is Going to Get One'': The Influence of Medical Brokering on Patient Candidacy for Total Joint Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Many patients in Ontario, despite being appropriate candidates for total joint arthroplasty (TJA), are not offered surgery. To understand this discrepancy, the authors sought to explore the process by which physicians determine patient candidacy for TJA. METHODS: Six focus groups (2 each of orthopedic surgeons, of rheumatologists, and of family physicians) and subsequent in-depth interviews were conducted with 50 practicing clinicians in Ontario. RESULTS: Health care system constraints, including extensive waiting lists, lack of homecare and postoperative support, and, for surgeons, access to operating rooms and resources, are perceived by physicians to routinely influence the ultimate choice of candidates for TJA. Medical brokering, defined as strategies used by physicians in a constrained health system to prioritize patients and to negotiate relationships with other physicians, was an important factor in determining candidacy for TJA. Because individual physicians and surgeons appear to use their own criteria for making these decisions, and because these criteria are modified from time to time in response to specific institutional and system conditions, brokering results in varied decisions about candidacy regardless of patient suitability. CONCLUSIONS: Lack of consensus on the necessary patient characteristics for TJA candidacy does not in and of itself account for the discrepancy between the number of patients who are suitable candidates for TJA and those who receive the procedure. Until the process by which health care system constraints affect and complicate the decision-making process around TJA candidacy is more fully explored, patients may not receive appropriate and timely access to this procedure.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".