Cardiac Enzymes after Transmyocardial Laser Treatment with CO<sub>2</sub>Laser
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to examine postoperative serum levels of cardiac enzymes after transmyocardial laser treatment (TML) and to evaluate any associations between this release, postoperative cardiac events and change in ejection fraction after 3 months' follow-up. DESIGN: Forty-nine patients with angina pectoris Canadian Cardiovascular Society Angina Score Class III & IV refractory to medical therapy and untreatable by coronary artery bypass or percutaneous transluminal angioplasty treated with CO2 laser were included. Inclusion criteria were age less than 75 years, left ventricular ejection fraction greater than or equal to 30% and myocardial regions with reversible ischemia. Serum levels of aspartate aminotranspherase (ASAT), alanine aminotranspherase (ALAT) and MB-isoenzymes of creatine kinase (CK-MB) were followed during the first 72 h after surgery. Ejection fractions were estimated by multiple-gated acquisition ventriculography at inclusion and 3 months postoperatively. RESULTS: A significant increase in serum markers of myocardial necrosis was observed 8 h after surgery. A subsequent increase from 8 to 24 h after surgery was associated with the presence of postoperative cardiac adverse events. An inverse correlation was found between peak level of cardiac enzymes and change in ejection fraction from baseline to 3 months' follow-up. CONCLUSIONS: TML with CO2 laser is followed by a significant increase in serum levels of cardiac enzymes after 8 h. Further significant increases are associated with cardiac adverse events postoperatively. Peak enzyme values are inversely correlated with change in ejection fraction from baseline to 3 months' follow-up.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".