Thirty-day readmission rate and discharge status following total hip arthroplasty using the supercapsular percutaneously-assisted total hip surgical technique
Bibliographic record
Abstract
PURPOSE: Recent studies have reported nearly 40% of costs associated with a 30-day episode-of-care for total joint replacements are due to post-discharge activities and 81% of those are specifically due to unplanned readmissions and discharging patients to post-acute care facilities. The purpose of this study was to determine these two key variables for total hip arthroplasty (THA) patients implanted using a tissue-sparing surgical technique and to see how these values compare to those previously reported in the United States. METHODS: The healthcare databases at three institutions were searched for primary THA patients implanted using the supercapsular percutaneously-assisted total hip (SuperPath) surgical technique between January 2013 and July 2014. Data elements included 30-day all-cause readmission rate, discharge status, transfusion rate, complications, and length of stay (LOS). RESULTS: Data were available for 479 THAs. The 30-day all-cause readmission rate, transfusion rate, and average LOS was 2.3, 3.3%, and 1.6 days, respectively. Over 91% of patients were discharged routinely home, 4.1% to skilled nursing facilities, 3.8% to home health care, and 0.6% to inpatient rehabilitation facilities. Complications included dislocation (0.8%), periprosthetic fracture (0.8%), and deep vein thrombosis (0.2 %). There were no infections reported. CONCLUSIONS: Patients implanted using this tissue-sparing technique experienced reduced 30-day all-cause readmission rates (2.3% vs. 4.2%) and more were routinely discharged home (91.5% vs. 27.3%) than have been previously reported for patients in the United States. Use of this tissue-sparing technique has the potential to significantly reduce post-discharge costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".