Caseload in rural general surgical practice and implications for training
Bibliographic record
Abstract
BACKGROUND: Despite increasing specialization within general surgery, many general surgeons, particularly in rural practice, continue to treat a wide range of conditions. The aim of the present paper was to provide accurate information on three rural surgeons' case-loads to illustrate the spectrum of surgery encountered and to assist in the planning of rural general surgical training. METHODS: A review was conducted of a prospectively maintained database of operations performed by three rural general surgeons in different parts of Victoria, Australia over a 5-year period. RESULTS: A large volume and wide range of procedures was performed by each surgeon, who averaged more than 500 operations per year (excluding endoscopies). Although most were within the range of procedures covered in the Royal Australasian College of Surgeons (RACS) Fellowship in general surgery, some encroached upon other specialties such as orthopaedics, urology, paediatric surgery and obstetrics/gynaecology. Operations outside of 'general' surgery reflected individual training and local community needs. CONCLUSIONS: The current RACS Fellowship in general surgery, augmented by training in other specialties as required, will help prepare general surgeons 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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".