Variable effects of obesity on access to total hip and knee arthroplasty
Bibliographic record
Abstract
Background: Obesity is an important comorbidity affecting outcomes after total joint arthroplasty. Consequently, surgeons may delay care of obese patients to first address obesity through different care pathways. The effect of obesity on patient wait times for total joint arthroplasty has not been explored. The purpose of this study was to evaluate the effect of obesity on access to total hip (THA) and knee (TKA) arthroplasty. Methods: The study data set was constructed from the Nova Scotia Health Authority's Horizon Patient Folder system and the Patient Access Registry Nova Scotia. Wait time was measured as days between the decision to treat and date of surgery. Body mass index (BMI) was calculated from a preoperative assessment, and patients were grouped into BMI categories. Multivariate log-linear regression was used to test for statistical differences, controlling for confounding factors. Results: We observed longer wait times for TKA with increasing BMI weight class. Patients with BMIs greater than 50 had 34% longer waits than reference weight patients. However, THA recipients showed no statistical difference in wait times across weight categories. Furthermore, there was variability among surgeons in the wait times experienced by patients. Conclusion: The finding of longer wait times for TKAs, but not THAs, among patients who were obese was unexpected. This shows the variable wait times for THA and TKA that patients who are obese can experience with different surgeons. It is important to understand the variability in wait times so that efforts to standardize the patient experience can be accomplished.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".