Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide a broad overview of the current state of knowledge of coronary artery bypass grafting with bilateral internal thoracic artery (BITA). RECENT FINDINGS: There exists a large body of literature from mostly observational studies supporting the use of BITA in patients undergoing coronary artery bypass grafting but selection bias is a major issue with nonrandomized data. The precise method of BITA use does not appear to impact graft patency nor clinical outcomes - in other words, BITA in any configuration appears to be protective. The major downside is the increased risk of sternal complications, which can be mitigated with sternal-sparring adjuncts. The 5-year interim results of the landmark Arterial Revascularization Trial comparing BITA versus single internal thoracic artery did not show a clinical benefit for BITA but the end-of-trial results are pending. Despite wide guideline support for BITA use, uptake in the surgical community remains low and this is likely because of technical and institutional barriers. SUMMARY: The published literature thus far supports surgical revascularization with BITA and we eagerly await the 10-year Arterial Revascularization Trial results. The general consensus is that a greater proportion of surgical revascularization should be performed using BITA.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".