Utility of the Vascular Quality Initiative in improving quality of care in Canadian patients undergoing vascular surgery
Bibliographic record
Abstract
The Vascular Quality Initiative (VQI) is a national cooperative quality-improvement initiative designed to evaluate processes of care and outcomes in vascular surgery. The purpose of this report is to show the utility of such a database to provide insight into the standard of care provided, to highlight areas of local quality improvement, to benchmark our data against local, regional and national trends, and to ultimately improve safety in Canadian patients undergoing vascular surgery. We present the history of the database, its spread in the Canadian health care system and examples of quality improvements achieved from analyses of data recorded and retrieved from the VQI. Using the VQI, our institution was able to decrease the length of stay after endovascular aneurysm repair, decrease the contrast volume in endovascular aneurysm repair, save on costs, and provide medium-term outcome data on peripheral vascular interventions and smoking cessation strategies. The VQI is a powerful tool to improve patient safety and quality in vascular surgery. Its ability to create local regional improvement groups fosters a quality-focused culture and is important for Canadian patients.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".