Converting hip and knee arthroplasty cases to same-day surgery due to COVID-19
Bibliographic record
Abstract
Aims In 2020, the COVID-19 pandemic meant that proceeding with elective surgery was restricted to minimize exposure on wards. In order to maintain throughput of elective cases, our hospital (St Michaels Hospital, Toronto, Canada) was forced to convert as many cases as possible to same-day procedures rather than overnight admission. In this retrospective analysis, we review the cases performed as same-day arthroplasty surgeries compared to the same period in the previous 12 months. Methods We conducted a retrospective analysis of patients undergoing total hip and knee arthroplasties over a three-month period between October and December in 2019, and again in 2020, in the middle of the COVID-19 pandemic. Patient demographics, number of outpatient primary arthroplasty cases, length of stay for admissions, 30-day readmission, and complications were collated. Results In total, 428 patient charts were reviewed for October to December of 2019 (n = 195) and 2020 (n = 233). Of those, total hip arthroplasties (THAs) comprised 60% and 58.8% for 2019 and 2020, respectively. Demographic data was comparable with no statistical difference for age, sex, contralateral joint arthroplasty, or BMI. American Society of Anesthesiologists grade I was more highly prevalent in the 2020 cohort (5.1-times increase; n = 13 vs n = 1). Degenerative disc disease and fibromyalgia were less significantly prevalent in the 2020 cohort. There was a significant increase in same day discharges for non-direct anterior approach THAs (two-times increase) and total knee arthroplasty (ten-times increase), with a reciprocal decrease in next day discharges. There were significantly fewer reported superficial wound infections in 2020 (5.6% vs 1.7%) and no significant differences in readmissions or emergency department visits (3.1% vs 3.0%). Conclusion The COVID-19 pandemic meant that hospitals and patients were hopeful to minimize the exposure to the wards, and minimize strain on the already taxed inpatient beds. With few positives during the COVID-19 crisis, the pandemic was the catalyst to speed up the outpatient arthroplasty programme that has resulted in our institution being more efficient, and with no increase in readmissions or early complications. Cite this article: Bone Jt Open 2021;2(7):545–551.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".