Evaluating the scope of rural general surgery in British Columbia
Bibliographic record
Abstract
Background: Rural general surgeons perform many procedures outside the conventional scope of the specialty. Unique to British Columbia, the Rural Practice Subsidiary Agreement (RSA) formally defines rurality in the province. Our goal is to understand the scope of practice for BC’s rural general surgeons and whether it has been affected over time by changing privileging guidelines. Methods: Medical Services Plan (MSP) data were collected from 2011 to 2021 for procedures billed by general surgeons in communities defined by the RSA as rural. We categorized codes from the MSP based on surgical specialty. For each community, we calculated the totals for these categories considering what other surgical specialties were present as well as changes over time. Results: From 2011 to 2021, 222 905 procedures were performed in 23 rural communities in BC. Colonoscopies were the most frequently performed procedure (n = 80 114, 35.9%), followed by colorectal (n = 23 891, 10.7%) and hernia procedures (n = 20 911, 9.4%). The most common unconventional procedures were plastic surgeries (n = 8077, 3.6%). Classification within the RSA did not significantly influence the percentage of unconventional general surgery procedures performed (p = 0.4). When another surgical specialty was present, there was often a decrease in the number of that specialty’s procedures performed by general surgeons. Over the past decade, rural general surgeons performed fewer unconventional general surgery procedures (p < 0.001). Conclusion: General surgeons working in rural communities perform a variety of procedures based on resources, community need, and access to other specialists. Over the last decade, this appears to have been influenced by new privileging guidelines. Understanding the scope of rural general surgery can inform training opportunities and, as there is a migration away from rural surgeons performing as many unconventional procedures, can elucidate the implications on patients and communities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".