The influence of the COVID-19 pandemic on total hip and knee arthroplasty in Ontario: a population-level analysis
Bibliographic record
Abstract
Background: The effects of the COVID-19 pandemic on elective orthopedic surgery have yet to be reported at the population level in Canada. We sought to detail the effect of the pandemic on patients who underwent total hip arthroplasty (THA) and total knee arthroplasty (TKA), and on surgeons with respect to surgical volume, wait times and health care quality. Method: We compared patient length of hospital stay, revisions, readmissions and emergency department presentations between pre-pandemic (April 2019 to February 2020) and postpandemic (April 2020 to February 2021) cohorts of patients who underwent inpatient THAs or TKAs. Wait times for THA and TKA in Ontario were similarly collected. Results: Case volumes for THA and TKA decreased by 30% during the pandemic. There were significantly fewer medically complex cases during this time period (p < 0.001). Length of hospital stay was reduced from 2.2 to 1.8 days (p < 0.001). Patients were less likely to visit the emergency department within 30 days of surgery (p < 0.001). Patients who underwent TKA were also more likely to be discharged directly home (p = 0.025). There was no difference in rate of revision surgery or readmission within 30 days. The proportion of patients meeting the standard benchmark wait time in Ontario was significantly lower (p < 0.001). The corresponding wait time to treatment increased significantly (p < 0.001). Conclusion: The effects of the COVID-19 pandemic on elective THA and TKA case volumes and wait times was significant. Patients having surgery during the pandemic were less medically complex, had shorter length of hospital stays and had significantly less health care utilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".