Consistent Factors Influence Body Mass Index Thresholds for Total Joint Arthroplasty Across Health-Care Systems
Bibliographic record
Abstract
BACKGROUND: Body mass index (BMI) thresholds are used as eligibility criteria to reduce complication risk in total joint arthroplasty (TJA). This approach oversimplifies preoperative risk assessment and inadvertently restricts access to effective surgical treatment for osteoarthritis. A prior survey of orthopaedic surgeons in the United States identified complex underlying factors that influence BMI considerations. To understand whether similar factors exist and influence surgeons in a different health-care system setting, we investigated Canadian surgeons' views and use of BMI criterion thresholds for TJA access. METHODS: A cross-sectional online qualitative survey was conducted with orthopaedic surgeons performing TJA in the Canadian health-care system. Responses were anonymous and questions were open-ended to allow for candid perspectives. Survey data were coded and a systematic process was followed to identify major themes. Findings were compared with U.S. surgeon perspectives. RESULTS: Sixty-nine respondents had a mean age of 49.0 ± 11.4 years (range, 33 to 79 years), with a mean surgical experience duration of 15.7 ± 11.4 years (range, 2 to 50 years). Surgeons reported variable use of BMI thresholds in practice. Twelve interconnected factors that influence BMI considerations were identified: (1) variable evidence interpretation, (2) surgical challenge, (3) surgeon beliefs and biases, (4) hospital differences, (5) access to resources, (6) health system bias, (7) patient health status, (8) patient body fat distribution, (9) patient decisional burden (to lose weight or accept risk), (10) evidence gaps and uncertainties, (11) need for innovation, and (12) societal views. Nine themes matched with findings from U.S. surgeons. CONCLUSIONS: Parallel to the United States, complex, interconnected factors influence Canadian orthopaedic surgeons' variable use of BMI restrictions for TJA eligibility. Despite different health-care systems and reimbursement models, similar technical and personal factors were identified. With TJA practice guidelines advising against hard BMI criteria, attention regarding access to resources, surgical training, and innovations to address TJA complexity in patients with large bodies are critically needed. Future advancements in this sphere must balance barrier removal with risk reduction to ensure safe and equitable surgical care. CLINICAL RELEVANCE: This study may influence surgeon behaviors with regard to hard BMI cutoffs for TJA and encourage critical thought about factors that influence decisions about surgical eligibility for patients with high BMI.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".