2006 William W. L. Glenn Lecture–Rebuilding the Heart: New Horizons for Cardiac Surgeons
Bibliographic record
Abstract
Cardiac surgery is changing today, as it was during the time of Dr William Glenn. In those days, cardiac surgeons were trained to do thoracic and vascular surgery.Today, cardiac surgeons are retraining to do intravascular and minimally invasive cardiac and vascular procedures. However, today’s cardiac surgeons should also retrain to employ gene-enhanced cell therapy to modify both the heart and the vasculature to improve the outcomes of their interventions. Cell transplantation has come of age and is undergoing extensive clinical trials. The implantation of precursor cells induces angiogenesis, improves regional and global function, and enhances the recruitment of reparative cells to the heart. In addition to correcting anatomic cardiac lesions, surgeons may be able to restore function to the heart by a combination of cardiac regeneration and rejuvenation of the response to injury. Tissue engineering may restore heart function without synthetic materials, provided the surgeon employs the right combination of cells and biodegradable scaffolds. These grafts may be ideal for the surgical repair of congenital cardiac defects, and clinical trials are underway. The implanted grafts grow and remodel as the child becomes an adult. Cardiac surgeons are retraining to acquire new skills to correct cardiovascular defects. In addition, surgeons should acquire the knowledge required to regenerate and rebuild the heart and vasculature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.018 |
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".