Recurrence of inguinal hernias repaired in a large hernia surgical specialty hospital and general hospitals in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The effect of hospital specialization on the risk of hernia recurrence after inguinal hernia repair is not well described. METHODS: We studied Ontario residents who had primary elective inguinal hernia repair at an Ontario hospital between 1993 and 2007 using population-based, administrative health data. We compared patients from a large hernia specialty hospital (Shouldice Hospital) with those from general hospitals to determine the risk of recurrence. RESULTS: We studied 235 192 patients, 27.7% of whom had surgery at Shouldice hospital. The age-standardized proportion of patients who had a recurrence ranged from 5.21% (95% confidence interval [CI] 4.94%-5.49%) among patients who had surgery at the lowest volume general hospitals to 4.79% (95% CI 4.54%-5.04%) who had surgery at the highest volume general hospitals. In contrast, patients who had surgery at the Shouldice Hospital had an age-standardized recurrence risk of 1.15% (95% CI 1.05%-1.25%). Compared with patients who had surgery at the lowest volume hospitals, hernia recurrence among those treated at the Shouldice Hospital was significantly lower after adjustment for the effects of age, sex, comorbidity and income level (adjusted hazard ratio 0.21, 95% CI 0.19-0.23, p < 0.001). CONCLUSION: Inguinal hernia repair at Shouldice Hospital was associated with a significantly lower risk of subsequent surgery for recurrence than repair at a general hospital. While specialty hospitals may have better outcomes for treatment of common surgical conditions than general hospitals, these benefits must be weighed against potential negative impacts on clinical care and the financial sustainability of general hospitals.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".