Documenting surgical triage in rural surgical networks: Formalising existing structures
Bibliographic record
Abstract
OBJECTIVE: It is essential that the embedded process of rural case selection be highlighted and documented to provide reassurance of rigour across rural surgical services supported by generalist surgeons, general practitioners with enhanced surgical skills and general practitioner anaesthetists. This enables feedback and improves the triage and case selection process to ensure the highest quality outcomes. Therefore, this research aims to explore participants' rational criteria for decision making around rural case selection. DESIGN: Participants participated in a series of semi-structured in-depth interviews which were coded and underwent thematic analysis. SETTING: Six community hospitals in British Columbia, Canada. PARTICIPANTS: General practitioners with enhanced surgical skills, general practitioner anaesthetists, local maternity care providers, and specialists. RESULTS: Based on participant accounts, rural surgical and obstetrical decision-making processes for local patient selection or regional referral had five major components: (1) Clinical Factors, (2) Physician Factors, (3) Patient Factors, (4) Consensus Between Providers and (5) the Availability of Local Resources. CONCLUSION: Decision-making processes around rural surgical and obstetrical patient selection are complex and require comprehensive understanding of local capacity and resources. Current policies and guidelines fail to consider the varying capacities of each rural site and should be hospital specific.
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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 | 0.045 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".