“I Often Feel Conflicted in Denying Surgery”: Perspectives of Orthopaedic Surgeons on Body Mass Index Thresholds for Total Joint Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Use of a patient body mass index (BMI) eligibility threshold for total joint arthroplasty (TJA) is controversial. A strict BMI criterion may reduce surgical complication rates, but over-restrict access to effective osteoarthritis (OA) treatment. Factors that influence orthopaedic surgeons' use of BMI thresholds are unknown. We aimed to identify and explore orthopaedic surgeons' perspectives regarding patient BMI eligibility thresholds for TJA. METHODS: A cross-sectional, online qualitative survey was distributed to orthopaedic surgeons who conduct hip and/or knee TJA in the United States. Survey questions were open-ended, and responses were collected anonymously. Survey data were coded and analyzed in an iterative, systematic process to identify predominant themes. RESULTS: Forty-five surveys were completed. Respondents were 54.3 ± 12.4 years old (range, 34 to 75 years), practiced in 22 states, and had 21.2 ± 13.3 years (range, 2 to 44 years) of surgical experience. Twelve factors influencing BMI threshold use by orthopaedic surgeons were identified: (1) evidence interpretation, (2) personal experiences, (3) difficulty of surgery, (4) professional ramifications, (5) ethics and biases, (6) health-system policies and performance metrics, (7) surgical capacity and resources, (8) patient body fat distribution, (9) patient self-advocacy, (10) control of decision-making in the clinical encounter, (11) expectations for demonstrated weight loss, and (12) research and innovation gaps. CONCLUSIONS: Multilevel, complex factors underlie BMI threshold use for TJA eligibility. Addressing identified factors at the patient, surgeon, and health-system levels should be considered to optimally balance complication avoidance with improving access to life-enhancing surgery. CLINICAL RELEVANCE: This study may influence how orthopaedic surgeons think about their own practices and how they approach patients and consider surgical eligibility.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".