Rural surgical and obstetric facility-level outcomes for index procedures: a retrospective cohort study (2016–2021)
Bibliographic record
Abstract
Background: Many rural communities have lost local access to procedural care, and although rural surgical services have endured in some regions, questions regarding quality and safety of care have persisted. Using retrospective observational data, we sought to compare adverse outcomes of the most common surgical procedures performed at rural facilities in British Columbia and outcomes by provider specialty. Our objective was to show whether the efficacy of surgical care at rural facilities is comparable to that of larger referral facilities and whether family physicians with enhanced surgical skills (FPESS) have outcomes comparable to those of specialists at referral facilities for low-morbidity patients. Methods: We included patients who had a colonoscopy, hernia repair, appendectomy, or cesarean delivery at 1 of 7 rural hospitals in BC that participated in the Rural Surgical and Obstetrics Networks of BC and corresponding referral facilities between 2016 and 2021. To control for differences in the acuity of patients between facility types, we risk stratified data by patient comorbidity level, in addition to adjusting for other demographic differences using multivariable Firth logistic regression analysis. We also compared the outcomes of FPESS with those of regional specialists for low-acuity patients in a similar manner. We calculated adjusted odds ratios (ORs), used tests of noninferiority to obtain p values for the adjusted ORs, and calculated E-values to estimate the extent to which our findings could be due to other unmeasured confounding. Results: Most surgical procedures at rural hospitals were performed by FPESS (n = 4403, 34.9%) and visiting general surgeons (n = 7317, 57.9%). We found that the quality of care at rural facilities was at least equivalent to the quality at referral facilities in rural BC for colonoscopy, hernia repair, and appendectomy, and that FPESS had outcomes at least equivalent to those of specialists for low-acuity patients. Conclusion: Our findings provide evidence in favour of the efficacy of rural procedural care at BC facilities, and although these results are not inherently generalizable to other populations, we believe they illustrate the potential for high-quality rural care for low-acuity procedures in similar settings. These findings are an important step toward documenting rural-specific outcomes and creating attendant benchmarks for rural practice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".