Consequences of delaying non-urgent surgeries during COVID-19: a population-based retrospective cohort study in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: To ensure sufficient resources to care for patients with COVID-19, healthcare systems delayed non-urgent surgeries to free capacity. This study explores the consequences of delaying non-urgent surgery on surgical care and healthcare resource use. DESIGN: This is a population-based retrospective cohort study. SETTING: This study took place in Alberta, Canada, from December 2018 to December 2021. PARTICIPANTS: Adult patients scheduled for surgery in Alberta during the study period were included. PRIMARY AND SECONDARY OUTCOMES MEASURES: The proportion of surgeries completed and surgery wait time were the primary outcomes. The secondary outcomes were healthcare resource use (hospital length of stay, emergency room visits and physician visits). The association between the primary outcomes and patient and surgery-related variables was explored using regression. RESULTS: There were 202 470 unique patients with 259 677 scheduled surgeries included. Fewer surgeries were completed throughout the pandemic compared with before; in the fourth wave, there was a decrease from 79% pre-COVID-19 to 67%. There was a decrease in wait time for those who had surgery completed during COVID-19 (from 105 to 69 days). Having surgery completed and the wait for surgery were associated with the geographical zone, COVID-19 wave, and the surgery type and priority. There was a decrease in all measures of healthcare resource use and an increase in hospital and all-cause mortality during COVID-19 compared with before COVID-19. CONCLUSIONS: The change in the proportion of scheduled surgeries completed and the wait time for completed surgery was modest and associated with COVID-19 wave and surgery-related variables, which was aligned with policies enacted during COVID-19 for surgery. The decrease in healthcare resource use suggests the effects of the COVID-19 pandemic may be delayed and may result in many patients presenting with advanced disease requiring surgical care.
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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 | 0.002 | 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) | 0.001 | 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".