THE INFLUENCE OF THE COVID-19 PANDEMIC ON TOTAL HIP AND KNEE ARTHROPLASTY IN ONTARIO: A POPULATION-LEVEL ANALYSIS
Bibliographic record
Abstract
The arrival of the novel coronavirus SARS-CoV-2 (COVID-19) in March 2020 and the global pandemic that ensued resulted in province wide cancellations of elective surgery. The impact of this has yet to be reported at the population level in Canada in a universial healthcare setting. The purpose of the present study was to provide a review of the impact of COVID-19 on THA and TKA on orthopaedic surgeons and patients in the province of Ontario. Specific aims of the study were to identify changes at the population level in surgical volume, wait times and healthcare quality. Pre and peri-COVID-19 THA and TKA cohorts were developed with aggregate data collected from the Canadian Institute for Healthcare Information and Canadian Joint Replacement Registry. Inclusion criteria consisted of an inpatient THA or TKA performed in adult Ontario citizens for diagnosis of osteoarthritis between April 2016 and February 2021. Quality was assessed via patient length of stay, revisions, readmissions and emergency department presentations. Wait times for THA and TKA were collected from CIHI's publicly available data for Ontario. Statistical analysis was performed comparing patient cohorts via χ 2 and t-tests. In the year following the beginning of the COVID-19 pandemic 27536 THA and TKAs were performed, representing a 30% reduction in case volume compared to the prior year. THA volume during this period decreased by 20%, while TKA volume decreased by 36%. There were significantly less medically complex underwent surgery during the COVID-19 time period (p =(p=0.025). Both THA and TKA patients were less likely to visit the emergency department within 30 days of surgery (p=in Ontario for surgical treatment shrunk from 85% in 2019 to 64% in 2020 for THA, while TKA decreased from 80% to 56% (p=< 0.001). The corresponding wait time to treatment increased by 64 days for THA and 78 days for TKA (p=< 0.001). The impact of COVID-19 on elective THA and TKA case volumes in Ontario during the 1st year of the COVID-19 pandemic was significant. We found an overall 30% decrease in primary THA and TKA volume between April 1, 2020 and March 1, 2021. This corresponded with an expected significant increase in surgical wait times for patients. Patients having surgery within the peri-COVID-19 time span were less medically complex, had shorter length of stays, were less likely to visit the emergency department following surgery and more likely to be discharged directly home.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".