Regionalization of services improves access to emergency vascular surgical care
Bibliographic record
Abstract
Management of vascular surgical emergencies requires rapid access to a vascular surgeon and hospital with the infrastructure necessary to manage vascular emergencies. The purpose of this study was to assess the impact of regionalization of vascular surgery services in Toronto to University Health Network (UHN) and St Michael's Hospital (SMH) on the ability of CritiCall Ontario to transfer patients with life- and limb-threatening vascular emergencies for definitive care. A retrospective review of the CritiCall Ontario database was used to assess the outcome of all calls to CritiCall regarding patients with vascular disease from April 2003 to March 2010. The number of patients with vascular emergencies referred via CritiCall and accepted in transfer by the vascular centers at UHN or SMH increased 500% between 1 April 2003-31 December 2005 and 1 January 2006-31 March 2010. Together, the vascular centers at UHN and SMH accepted 94.8% of the 1002 vascular surgery patients referred via CritiCall from other hospitals between 1 January 2006 and 31 March 2010, and 72% of these patients originated in hospitals outside of the Toronto Central Local Health Integration Network. Across Ontario, the number of physicians contacted before a patient was accepted in transfer fell from 2.9 ± 0.4 before to 1.7 ± 0.3 after the vascular centers opened. In conclusion, the vascular surgery centers at UHN and SMH have become provincial resources that enable the efficient transfer of patients with vascular surgical emergencies from across Ontario. Regionalization of services is a viable model to increase access to emergent care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".