Cadaveric Research: Informing Current Heart Surgery Techniques
Bibliographic record
Abstract
Cadavers have long been used in the anatomy lab as teaching tools however, they can also provide valuable research that addresses common surgical problems. Aortic root enlargement (ARE) procedures are employed to facilitate the implantation of larger valve prostheses however, published evidence remains largely anecdotal with respect to the specific increases achievable with these techniques. We sought to design a study using a cadaveric model that will allow for a controlled experiment to evaluate the efficacy of commonly employed ARE techniques. We harvested 20 adult hearts and stratified them into four groups based on aortic annular diameter. Each heart underwent a bileaflet mechanical valve implantation following four ARE techniques with the appropriate reversals between procedures. Annular diameters and implanted prosthesis sizes were recorded following each enlargement technique. We performed all four techniques on each heart with little disruption to the remaining aortic root tissue. In light of the study design, each heart served as its own control. We were also able to provide data as to the increases in implanted prosthesis size achieved with the four ARE techniques. Despite the limitations, cadaveric specimens can play a key role in cardiac surgical research. They provide important data for preoperative planning and allow for experimental research without patient risk. Grant Funding Source : Queen Elizabeth II Graduate Scholarship in Science and Technology
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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 | 0.055 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".