Preoperative cardiovascular risk factor control in elective coronary artery bypass graft patients: a failure of present management.
Bibliographic record
Abstract
BACKGROUND: After coronary artery bypass graft (CABG) patients are at high risk for disease progression and future cardiac events. Risk factor control can reduce subsequent clinical events and mortality. The appropriateness of cardiovascular risk factor management in CABG patients is largely unknown. OBJECTIVES: To evaluate the presence of cardiovascular risk factors, their treatment and the adequacy of that treatment in patients just before elective CABG PATIENTS AND METHODS: Over a six-month period in 1999, 120 patients who underwent elective CABG at a single centre were assessed. All patients were assessed for the presence of important, known, modifiable cardiovascular risk factors (smoking, hypertension, hypercholesterolemia, obesity and diabetes), and the adequacy of the control of these risk factors, as determined by published consensus conference guidelines. RESULTS: Ninety-five per cent of patients were receiving treatment for their risk factors. Twenty of 86 patients had their hyperlipidemia controlled, only 10 of 36 patients with diabetes had their glucose well controlled, 56 of 82 patients had adequate control of their hypertension, 21 of 120 patients were current smokers, 78 of 120 patients were obese and only 13 of 120 patients had all risk factors under control. CONCLUSIONS: As expected, the prevalence of all the risk factors was very high. Despite a high level of medical treatment, risk factor management was very poor. More effort needs to go into active, long term management, and patient education and motivation, if any substantial progress is to be made in reducing future cardiac events in patients after CABG.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".