Scheduled and urgent inguinal hernia repair in Ontario, Canada between 2010 and 2022: Population-based cross sectional analysis of trends and outcomes
Bibliographic record
Abstract
INTRODUCTION: We examine trends in inguinal hernia repairs with respect to the COVID-19 pandemic and secular trends in Ontario, Canada. METHODS: This was a retrospective cohort study. Hernia repairs performed January 1, 2010-December 31, 2022 were captured from health administrative inpatient and outpatient databases. Patients managed in three clinical settings were examined: public hospital in-patient, semi-private hospital in-patient (Shouldice Hospital), and public hospital out-patient. We examined the effect of the COVID-19 pandemic on surgical volumes, clinical setting, patient characteristics by setting, time from diagnosis until surgery, hospital length-of-stay, and patient outcomes (90-day readmissions, 1-year reoperations). We used multivariable logistic regression to examine whether patient outcomes were comparable between the COVID-19 period and the pre-pandemic period, adjusted sociodemographic and clinical factors. Shouldice Hospital is the only semi-private hospital in Ontario specializing in hernia repair (patients pay for the mandated admission, but not for the procedure). RESULTS: During the pandemic (March 2020-December 2022), there were 8,162 fewer (15%) scheduled inguinal hernia repairs than expected, but the age-sex standardized rate of urgent repairs remained unchanged. Shouldice Hospital performed more surgeries in the COVID-19 era than pre-pandemic and had a shorter average LOS by 24 hours, despite treating more patients with older age, higher ASA score [adjusted odds ratio (aOR) 2.13 (1.93-2.35) III vs I-II] and greater comorbidity [aOR 1.36 (1.08-1.70) for 2 vs none] than pre-pandemic. Patients treated in the COVID-19 era experienced a longer time until surgery, being the longest in 2022 (median 133 days). Ninety-day readmissions and 1-year reoperations were lower in the COVID-19 era and lower for patients receiving surgery at Shouldice Hospital. CONCLUSION: During the COVID-19 pandemic, there were 8,162 fewer scheduled hernia repairs than expected, longer wait-times until surgery, shorter length-of-stay, and more patients with comorbidities, but outcomes were not worse compared with the pre-pandemic period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".