Preoperative physical therapy results in shorter length of stay and discharge disposition following total knee arthroplasty: A retrospective study
Bibliographic record
Abstract
Objective: Total knee arthroplasty is an effective surgical approach used to treat arthritis and knee trauma. Its utilization has grown, as has the accompanying financial impact, resulting in an equal need to advance physical therapy practice. One emerging approach improving patient outcomes and reducing cost is the inclusion of a preoperative physical therapy visit. The aim of this study was to quantify the economic impact of a standardized preoperative physical therapy visit in the healthcare setting. Design: This study is a retrospective review of 1,043 adult patients who underwent total knee arthroplasty. Methods: Patients who underwent total knee arthro-plasty were divided into those who received a prehab compared with those who did not. Results: Preoperative physical therapy resulted in a marked decrease in length of stay, with 37.1% of preoperative physical therapy patients leaving inpatient care on post-operative day 1 compared to 27.0% of the no preoperative physical therapy controls (p???0.001). Preoperative physical therapy also improved discharge disposition, with 41.6% of preoperative physical therapy patients returning home and utilizing outpatient services compared to 23.2% of controls (p???0.001). No effect on duration of care was observed. Conclusion: These data suggest that a single preoperative physical therapy visit improves key outcomes, both clinically and financially, following total knee arthroplasty.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".