North American lower-extremity revascularization and amputation during COVID-19: Observations from the Vascular Quality Initiative
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic’s impact on vascular procedural volumes and outcomes has not been fully characterized. Methods: Volume and outcome data before (1/2019 – 2/2020), during (3/2020 – 4/2020), and following (5/2020 – 6/2020) the initial pandemic surge were obtained from the Vascular Quality Initiative (VQI). Volume changes were determined using interrupted Poisson time series regression. Adjusted mortality was estimated using multivariable logistic regression. Results: The final cohort comprised 57,181 patients from 147 US and Canadian sites. Overall procedure volumes fell 35.2% (95% CI 31.9%, 38.4%, p < 0.001) during and 19.8% (95% CI 16.8%, 22.9%, p < 0.001) following the surge, compared with presurge months. Procedure volumes fell 71.1% for claudication (95% CI 55.6%, 86.4%, p < 0.001) and 15.9% for chronic limb-threatening ischemia (CLTI) (95% CI 11.9%, 19.8%, p < 0.001) but remained unchanged for acute limb ischemia (ALI) when comparing surge to presurge months. Adjusted mortality was significantly higher among those with claudication (0.5% vs 0.1%; OR 4.38 [95% CI 1.42, 13.5], p = 0.01) and ALI (6.4% vs 4.4%; OR 2.63 [95% CI 1.39, 4.98], p = 0.003) when comparing postsurge with presurge periods. Conclusion: The first North American COVID-19 pandemic surge was associated with a significant and sustained decline in both elective and nonelective lower-extremity vascular procedural volumes. When compared with presurge patients, in-hospital mortality increased for those with claudication and ALI following the surge.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".