Non-invasive cardiac stress testing before elective major non-cardiac surgery: population based cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine the association of non-invasive cardiac stress testing before elective intermediate to high risk non-cardiac surgery with survival and hospital stay. DESIGN: Population based retrospective cohort study. SETTING: Acute care hospitals in Ontario, Canada, between 1 April 1994 and 31 March 2004. PARTICIPANTS: Patients aged 40 years or older who underwent specific elective intermediate to high risk non-cardiac surgical procedures. INTERVENTIONS: Non-invasive cardiac stress testing performed within six months before surgery. MAIN OUTCOME MEASURES: Postoperative one year survival and length of stay in hospital. RESULTS: Of the 271 082 patients in the entire cohort, 23 991 (8.9%) underwent stress testing. After propensity score methods were used to reduce important differences between patients who did or did not undergo preoperative stress testing and assemble a matched cohort (n=46 120), testing was associated with improved one year survival (hazard ratio (HR) 0.92, 95% CI 0.86 to 0.99; P=0.03) and reduced mean hospital stay (difference -0.24 days, 95% CI -0.07 to -0.43; P<0.001). In an analysis of subgroups defined by Revised Cardiac Risk Index (RCRI) class, testing was associated with harm in low risk patients (RCRI 0 points: HR 1.35, 95% CI 1.05 to 1.74), but with benefit in patients who were at intermediate risk (RCRI 1-2 points: 0.92, 95% CI 0.85 to 0.99) or high risk (RCRI 3-6 points: 0.80, 95% CI 0.67 to 0.97). CONCLUSIONS: Preoperative non-invasive cardiac stress testing is associated with improved one year survival and length of hospital stay in patients undergoing elective intermediate to high risk non-cardiac surgery. These benefits principally apply to patients with risk factors for perioperative cardiac complications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".