Short stay total joint arthroplasty program: patient factors predicting readmission
Bibliographic record
Abstract
Background: The aim of this study was to evaluate the effectiveness of our short stay arthroplasty program as measured by 30-day readmission rate and the rate of transfer to inpatient care. Risk factors for readmission/transfer were also evaluated and contrasted with current patient screening criteria. Methods: We retrospectively reviewed 297 charts for all primary total joint arthroplasties completed in the short stay program during an 18-month period. Data included readmission and patient characteristics such as age, sex, comorbidities, the American Society of Anesthesiologists (ASA) physical classification grade, body mass index (BMI) and the number of preoperative medications. Results: The 30-day readmission rate was 2.6% (n = 8). With the inclusion of patients transferred to the inpatient hospital, the overall failure rate of our short stay program was 6.7% (n = 20). Multivariable modelling controlling for age, BMI and ASA suggested that those with an in-hospital complication were 11.4 times more likely to be readmitted or transferred to inpatient care (p < 0.001) with a trend for patients who were taking more medications (p = 0.09). Conclusion: The current readmission rate from this program is comparable to previously published data in the arthroplasty literature. However, several patients required transfer to inpatient care, which significantly impacted the effectiveness of the short stay program. Risk factors for readmission/transfer are not completely accounted for by current presurgical screening criteria. Further evaluation of the Blaylock Risk Assessment Screening Score is required to determine its value for predicting hospital readmission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".