Potential Access to Emergency General Surgical Care in Ontario
Bibliographic record
Abstract
Limited access to timely emergency general surgery (EGS) care is a probable driver of increased mortality and morbidity. Our objective was to estimate the portion of the Ontario population with potential access to 24/7 EGS care. Geographic information system-based network-analysis was used to model 15-, 30-, 45-, 60-, and 90-min land transport catchment areas for hospitals providing EGS care, 24/7 emergency department (ED) access, and/or 24/7 operating room (OR) access. The capabilities of hospitals to provide each service were derived from a prior survey. Population counts were based on 2016 census blocks, and the 2019 road network for Ontario was used to determine speed limits and driving restrictions. Ninety-six percent of the Ontario population (n = 12,933,892) lived within 30-min’s driving time to a hospital that provides any EGS care. The availability of 24/7 EDs was somewhat more limited, with 95% (n = 12,821,747) having potential access at 30-min. Potential access to all factors, including 24/7 ORs, was only possible for 93% (n = 12,471,908) of people at 30-min. Populations with potential access were tightly clustered around metropolitan centers. Supplementation of 24/7 OR capabilities, particularly in centers with existing 24/7 ED infrastructure, is most likely to improve access without the need for new hospitals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".