Bilateral internal thoracic artery grafting: in situ or composite?
Bibliographic record
Abstract
Bilateral internal thoracic artery (BITA) grafting is considered a superior choice for coronary artery bypass grafting (CABG). While the 10-year outcomes of BITA grafting from the recent Arterial Revascularization Trial (ART) are still pending, numerous observational studies have demonstrated the advantages of BITA grafting. These include better long-term graft patency and freedom from arteriosclerosis, in addition to higher survival rate compared to CABG using only the left internal thoracic artery (ITA). The different BITA configurations are in situ and composite—the choice of optimal grafting configuration is challenging. Patient factors such as coronary anatomy, presence of a diseased ascending aorta and the potential need for a future redo sternotomy will influence the choice of the grafting strategy. In situ BITA grafting is associated with excellent clinical outcomes and has been extensively described in the literature. However, uncertainties remain regarding the ideal in situ configuration and design. Composite BITA grafting is the other option that maximizes right ITA (RITA) utilization. In this configuration, the RITA is able to reach the distal circumflex and right coronary artery branches. This approach decreases the need for a third graft conduit.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".