Not all total joint replacement patients are created equal: preoperative factors and length of stay in hospital
Bibliographic record
Abstract
BACKGROUND: We conducted a cross-sectional study of primary total joint replacement (TJR) patients to determine predictors for prolonged length of stay (LOS) in hospital to identify patient characteristics that may inform resource allocation, accounting for patient complexity. METHODS: Preoperative demographics, medical comorbidities and acute hospital LOS from a consecutive series of primary TJR patients from an academic arthroplasty centre were abstracted. We categorized patients as LOS of 3 or fewer days, 4 days, or 5 or more days to align results with varying LOS benchmarks. To identify predictors for LOS, we used a generalized logistic regression model fitted on an LOS ternary outcome, using LOS of 3 or fewer days as a reference category. RESULTS: The sample included 1459 patients: 61.7% total knee and 38.3% total hip. Male sex was predictive of an LOS of 3 or fewer days (4 d: odds ratio [OR] 0.48, 95% confidence interval [CI] 0.364-0.631; ≥ 5 d: OR 0.57, 95% CI 0.435-0.758), as was current smoking status (4 d: OR 0.425, 95% CI 0.274-0.659; ≥ 5 d: OR 0.489, 95% CI 0.314-0.762). Strong predictors of prolonged LOS included total hip versus total knee arthroplasty, age 75 years or older, American Society of Anesthesiologists classification of 3 and 4 and number of cardiovascular comorbidities. CONCLUSION: Not all patients undergoing TJR are equal. The goal should be individual patient-focused care rather than a predetermined LOS that is not achievable for all patients. Hospital resource planning must account for patient complexity when planning future bed management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".