Septal ethanol ablation for hypertrophic obstructive cardiomyopathy: early and intermediate results of a Canadian referral centre.
Bibliographic record
Abstract
BACKGROUND: Septal ethanol ablation (SEA) is a relatively new interventional nonsurgical treatment for patients with hypertrophic obstructive cardiomyopathy (HOCM). This procedure involves targeted infarction of the basal interventricular septum to reduce left ventricular outflow tract (LVOT) obstruction. OBJECTIVES: To describe the experience with this technique in a large tertiary care centre. METHODS AND RESULTS: Since 1998, 40 HOCM patients with disabling symptoms refractory to medical treatment have undergone SEA. Procedural success was 88% (35 of 40 patients). The LVOT gradient decreased from 86+/-38 mmHg to 16+/-16 mmHg. There were two major complications: one patient died of respiratory failure at 30 days following SEA, and one patient developed a major coronary dissection during the procedure and required emergency myectomy and coronary bypass surgery. There were two late failures (6% of initially successful cases). In both patients, the LVOT gradient and symptoms reappeared some months after the procedure and further interventions were required. In the remaining patients, the gradient continued to decrease to one year; 86% were asymptomatic or have mild symptoms compared with 94% with severe symptoms before SEA. Septal thickness decreased from 20.8+/-2.9 mm to 13.2+/-3.3 mm (P<0.001) at the site of the targeted septal infarct. CONCLUSION: SEA is a feasible option for suitable patients with HOCM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".