A Population-based Analysis of the COVID-19 Generated Surgical Backlog and Associated Emergency Department Presentations for Inguinal Hernias and Gallstone Disease
Bibliographic record
Abstract
OBJECTIVE: To evaluate the downstream effects of the COVID-19 generated surgical backlog. BACKGROUND: Delayed elective surgeries may result in emergency department (ED) presentations and the need for urgent interventions. METHODS: Population-based repeated cross-sectional study utilizing administrative data. We quantified rates of elective cholecystectomy and inguinal hernia repair and rates of ED presentations, urgent interventions, and outcomes during the first and second waves of COVID-19 (March 1, 2020- February 28, 2021) as compared to a 3-year pre-COVID-19 period (January 1, 2017-February 29, 2020) in Ontario, Canada. Poisson generalized estimating equation models were used to predict expected rates during COVID-19 based on the pre-COVID-19 period. The ratio of observed (actual events) to expected rates was generated for surgical procedures (SRRs) and ED visits (ED-RRs). RESULTS: We identified 74,709 elective cholecystectomies and 60,038 elective inguinal hernia repairs. During the COVID-19 period, elective inguinal hernia repairs decreased by 21% (SRR 0.791; 0.760-0.824) whereas elective cholecystectomies decreased by 23% (SRR 0.773; 0.732-0.816). ED visits for inguinal hernia decreased by 17% (ED-RR 0.829; 0.786 - 0.874) whereas ED visits for gallstones decreased by 8% (ED-RR 0.922; 0.878 - 0.967). A higher population rate of urgent cholecystectomy was observed, particularly after the first wave (SRR 1.076; 1.000-1.158). No difference was seen in inguinal hernias. CONCLUSIONS: An over 20% reduction in elective surgeries and an increase in urgent cholecystectomies was observed during the COVID-19 period suggesting a rebound effect secondary to the surgical backlog. The COVID-19 generated surgical backlog will have a heterogeneous downstream effect with significant implications for surgical recovery planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".