Enhanced recovery short-stay hip and knee joint replacement program improves patients outcomes while reducing hospital costs
Bibliographic record
Abstract
INTRODUCTION: An attractive option to reduce hospital length of stay (LOS) after hip or knee joint replacement (THA, TKA) is to follow the Enhanced Recovery After Surgery principles (ERAS) to improve patient experience to a level where they will feel confident to leave for home earlier. The objective of this study was to evaluate the implementation of short-stay protocol following the ERAS principles. HYPOTHESIS: We hypothesized that our ERAS THA and TKA short-stay protocol would result in a lower complication rate, shorter hospital LOS and reduced direct health care costs compared to our standard procedure. MATERIAL AND METHODS: We compared the complications rated according to Clavien-Dindo scale, hospital LOS and costs of the episode of care between a prospective cohort of 120 ERAS short-stay THA or TKA and a matched historical control group of 150 THA or TKA. RESULTS: Significantly lower rate of Grade 1 and 2 complications in the ERAS short-stay group compared with the standard group (mean 0.8 vs 3.0, p<0.001). No difference was found between the 2 groups for Grade 3, 4, or 5 complications. The mean hospital LOS for the ERAS short-stay group decreased by 2.8 days for the THAs (0.1 vs 2.9 days, p<0.001) and 3.9 days for the TKAs (1.0 vs 4.9 days, p<0.001). The mean estimated direct health care costs reduction with the ERAS short-stay protocol was 1489 CAD per THA and 4158 CAD per TKA. DISCUSSION: In many short-stay protocols, focus has shifted from ERAS goals of a reduction in complications and improved recuperation to use length of stay as the main factor of success. Implementation of an ERAS short-stay protocol for patients undergoing THA or TKA at our institution resulted not only in reduced hospital LOS, but also in improved patient care and reduced direct health care costs. LEVEL OF EVIDENCE: Level II.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".