A rare case of successful recanalization of the anterior interventricular artery through a mammary-coronary graft in a patient 15 years after coronary artery bypass grafting
Bibliographic record
Abstract
Progression of atherosclerosis in patients with coronary artery disease (CAD) who underwent surgical treatment, manifests itself in the development of dysfunction of the shunts, and in worsening of the condition of native coronary arteries. Accordingly, there is an increase in the number of patients who require repeated aggressive treatments. In cases where it is technically possible to perform repeated coronary artery bypass graft (CABG) and/or percutaneous interventions (PCI), there is no question of treatment tactics. But there are times when implementation of interventions is associated with a high risk and optimal medication therapy does not have the proper effect. In a patient with multiple lesions of the coronary arteries 15 years after CABG in connection with the progression of atherosclerosis, the occlusion of anterior interventricular artery (AIVA) is distal to the mammary-coronary anastomosis, the occlusion of the venous shunt to the right coronary artery. Effort angina (class III) is caused by myocardial ischemia in the AIVA territory. Patient underwent surgery for recanalization of AIVA through mammary-coronary shunt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".