Surgery in the western Canadian Arctic: The relative impact of family physicians with enhanced surgical skills working collaboratively with specialist surgeons
Bibliographic record
Abstract
Introduction: Little is known about the surgical needs of rural, remote or circumpolar populations in Canada; these same regions are also home to half of all Indigenous people in the country. In the present study, we sought to understand the relative impact of family physicians with enhanced surgical skills (FP-ESS) and Specialist Surgeons in the surgical care of a mostly Indigenous rural and remote community in the western Canadian Arctic. Methods: A descriptive and retrospective quantitative study was conducted to determine the number and range of procedures performed for the defined catchment population of the Beaufort Delta Region of the Northwest Territories, as well as the type of surgical provider and location of that service, over the 5 years from 1 April, 2014, to 31 March, 2019. Results: FP-ESS physicians in Inuvik performed 79% of all endoscopic and 22% of all surgical procedures, which accounted for nearly half of the total procedures performed. Over 50% of all procedures were performed locally (47.7% by FP-ESS and 5.6% by visiting specialist surgeons). For surgical cases alone, nearly one-third were performed locally, one-third in Yellowknife and the remaining one-third out-of-territory. Conclusions: This networked model reduces the overall demand on surgical specialists, who can better focus their efforts on surgical care that is beyond the scope of FP-ESS. With nearly half of the procedural needs of this population being met locally by FP-ESS, there are decreased health-care costs, better access and more surgical care closer to home.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".