The BASH score: A novel predictor for optimizing discharge timing in hip and knee arthroplasty
Bibliographic record
Abstract
Background Total knee and hip arthroplasty procedures are increasingly transitioning to outpatient settings, emphasizing the need for precise discharge planning to optimize patient safety and healthcare resource utilization. Traditional risk assessments, such as the Blaylock Risk Assessment Screening Score (BRASS), provide a foundation for identifying patients at risk of prolonged hospital stays. This study evaluates BRASS's role in predicting discharge outcomes and introduces the BASH score, a refined tool to enhance discharge planning in modern joint arthroplasty. Methods This retrospective cohort study assessed 447 patients undergoing primary total knee or hip arthroplasty for osteoarthritis. The BASH score was developed based on multivariable logistic regression modeling, incorporating BRASS, age, sex, and arthroplasty type. Additional evaluations included body mass index (BMI), the Pictorial Fit-Frail Scale (PFFS), and surgical timing. Each factor's predictive value for same-day discharge was assessed using simple and multivariable logistic regression models, with results validated using bootstrapping. Results The BASH score significantly predicted same-day discharge, with a median score of 4.0 (IQR 3.5–6.0, p < 0.001). Patients with higher BASH scores were less likely to achieve same-day discharge (OR 2.47, 95 % CI 1.46–4.15). Among evaluated factors, BMI and most PFFS components, excluding pain, did not robustly predict same-day discharge. Multivariable analysis demonstrated an R 2 of 0.113, with bootstrapped models confirming stability (Hosmer-Lemeshow goodness-of-fit p = 0.612). Conclusion The BASH score provides a simplified and effective tool for predicting same-day discharge in joint arthroplasty patients. By incorporating key predictive factors, including BRASS, age, sex, and arthroplasty type, the BASH score enhances discharge planning and resource allocation. However, further prospective studies are needed to validate its utility across diverse clinical settings. Next steps include prospectively assessing the utility of this scoring system in multiple centres.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".