Ambulatory Cardiology or General Internal Medicine Assessment Before Scheduled Major Vascular Surgery Is Associated with Improved Outcomes
Bibliographic record
Abstract
OBJECTIVE: To characterize the association between ambulatory cardiology or general internal medicine (GIM) assessment before surgery and outcomes after scheduled major vascular surgery. BACKGROUND: Cardiovascular risk assessment and management before high-risk surgery remains an evolving area of care. METHODS: This is a population-based retrospective cohort study of all adults who underwent scheduled major vascular surgery in Ontario, Canada, from April 1, 2004 to March 31, 2019. Patients who had an ambulatory cardiology and/or GIM assessment within 6 months before surgery were compared with those who did not. The primary outcome was 30-day mortality. Secondary outcomes included: composite of 30-day mortality, myocardial infarction or stroke, 30-day cardiovascular death, 1-year mortality, composite of 1-year mortality, myocardial infarction or stroke, and 1-year cardiovascular death. Cox proportional hazard regression using inverse probability of treatment weighting was used to mitigate confounding by indication. RESULTS: Among 50,228 patients, 20,484 (40.8%) underwent an ambulatory assessment before surgery: 11,074 (54.1%) with cardiology, 8071 (39.4%) with GIM, and 1339 (6.5%) with both. Compared with patients who did not, those who underwent an assessment had a higher Revised Cardiac Risk Index [N with Index over 2 = 4989 (24.4%) vs 4587 (15.4%), P < 0.001] and more frequent preoperative cardiac testing [N = 7772 (37.9%) vs 6113 (20.6%), P < 0.001], but lower 30-day mortality [N = 551 (2.7%) vs 970 (3.3%), P < 0.001]. After the application of inverse probability of treatment weighting, cardiology or GIM assessment before surgery remained associated with a lower 30-day mortality [weighted hazard ratio (95% CI) = 0.73 (0.65-0.82)] and a lower rate of all secondary outcomes. CONCLUSIONS: Major vascular surgery patients assessed by a cardiology or GIM physician before surgery have better outcomes than those who are not. Further research is needed to better understand potential mechanisms of benefit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".