Influence of the Availability of Laser Transmyocardial Revascularisation on Surgical Strategy in Patients with Advanced Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVE: To compare the planned and actual revascularisation techniques used in patients undergoing cardiac surgery for refractory angina when excimer transmyocardial laser revascularisation (TMR) is available. METHODS: Observational series of 31 patients (mean age 65 years) with severe angina [mean CCS score (SD) 3.8 (0.4)] and three-vessel coronary artery disease judged unsuitable for conventional bypass surgery alone. All patients underwent cardiac surgery: revascularisation techniques were determined by the operative findings. RESULTS: Pre-operative strategy was altered by the intra-operative findings in 13 patients (42%). In 5 (16%), the coronary vessels proved graftable and TMR was unnecessary. Conversely, in 6 patients (19%) an anticipated graft could not be performed and TMR was used as an alternative. In 2 patients (7%), neither strategy was possible. Overall, TMR was performed as a stand-alone procedure in 9 (29%) and combined with CABG in 17 (55%). Operative mortality was low: 0% at 30 days and 6% at 6 months. Mean CCS class (SD) improved post-operatively from 3.8 (0.4) to 1.7 (1.1) (p < 0.01). CONCLUSIONS: The pre-operative coronary angiogram is an imperfect predictor of which coronary vessels are suitable for grafting. The availability of laser TMR allows the cardiac surgeon to accept cases which would otherwise be considered inoperable and to respond better to intraoperative findings. The combination of laser TMR and bypass grafts provides good short- and medium-term symptomatic improvement with a low post-operative mortality.
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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 | 0.005 |
| 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 | 0.001 | 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, 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".