Enhanced Recovery after Surgery for Knee Arthroplasty in the Era of COVID-19
Bibliographic record
Abstract
Enhanced recovery after surgery (ERAS) represents a paradigm shift in perioperative care, aimed at achieving early recovery for surgical patients, reducing length of hospital stay, and complications. The purpose of this study was to provide an insight of the impact of the COVID-19 on ERAS protocols for knee arthroplasty patients in a tertiary hospital and potential strategy changes for postpandemic practice. We retrospectively reviewed all cases that underwent surgery utilizing ERAS protocols in the quarter prior to the pandemic (fourth quarter of 2019) and during the first quarter of 2020 when the pandemic started. A review of the literature on ERAS protocols for knee arthroplasty during the COVID-19 pandemic was also performed and discussed. A total of 199 knee arthroplasties were performed in fourth quarter of 2019 as compared with 76 in the first quarter of 2020 during the COVID-19 outbreak. Patients who underwent surgery in the first quarter of 2020 had shorter inpatient stays (3.8 vs. 4.5 days), larger percentage of discharges by postoperative day 5 (86.8 vs. 74.9%), and a larger proportion of patients discharged to their own homes (68 vs. 54%). The overall complication rate (1.3 vs. 3%) and readmission within 30 days (2.6 vs. 2%) was similar between both groups. ERAS protocols appear to reduce hospital lengths of stay for patients undergoing knee arthroplasty without increasing the risk of short-term complications and readmissions. The beneficial effects of ERAS appear to be amplified by and are synchronous with the requirements of operating in the era of a pandemic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".