Transmyocardial Laser Revascularization: A Consensus Statement of the International Society of Minimally Invasive Cardiothoracic Surgery (ISMICS) 2006
Bibliographic record
Abstract
INTRODUCTION Dramatic advances in coronary revascularization techniques have been developed in recent years. Improved technologies and techniques for coronary artery surgery and percutaneous coronary intervention have allowed for treatment of coronary vessels that were previously considered inaccessible or not amenable to conventional treatment.1 In addition, pharmacologic management of chronic angina has improved clinical outcomes.2 Despite advances in pharmacologic therapies and catheter-based or surgical revascularization techniques, a significant number of patients with angina are poor candidates for traditional methods of treatment because of diffuse coronary disease, small distal vessels, or other comorbidities.3 Additional options are required for patients with angina refractory to maximal medical therapy who are not candidates for catheter-based therapy or bypass surgery or in whom these methods have failed.4 Whether transmyocardial laser revascularization (TMR) represents a potential option for these patients has been explored in a number of clinical trials. Given that the evidence base has begun to grow in this area and considering that decisions will need to be made regarding uptake of this procedure into practice, there is a need for international consensus regarding the evidence for the balance of benefit and risks of TMR relative to continued maximal medical therapy (MMT).
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".