Cardiac assessment prior to non‐cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Increasingly, patients undergoing non-cardiac surgery are older and have more comorbidities yet preoperative cardiac assessment appears haphazard and unsystematic. We hypothesised that patients at high cardiac risk were not receiving adequate cardiac assessment, and patients with low-cardiac risk were being over-investigated. AIMS: To compare in a representative sample of patients undergoing non-cardiac surgery the use of cardiac investigations in patients at high and low preoperative cardiac risk. METHODS: We examined cardiac assessment patterns prior to elective non-cardiac surgery in a representative sample of patients. Cardiac risk was calculated using the Revised Cardiac Risk Index. RESULTS: Of 671 patients, 589 (88%) were low risk and 82 (12%) were high risk. We found that nearly 14% of low-risk and 45% of high-risk patients had investigations for coronary ischaemia prior to surgery. Vascular surgery had the highest rate of investigation (38%) and thoracic patients the lowest rate (14%). Whilst 78% of high-risk patients had coronary disease, only 46% were on beta-blockers, 49% on aspirin and 77% on statins. For current smokers (17.3% of cohort, n = 98), 60% were advised to quit pre-op. CONCLUSIONS: Practice patterns varied across surgical sub-types with low-risk patients tending to be over-investigated and high-risk patients under-investigated. A more systemised approach to this large group of patients could improve clinical outcomes, and more judicious use of investigations could lower healthcare costs and increase efficiency in managing this cohort.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".