eComment. Marginal hearts: a second-round draft picks?
Bibliographic record
Abstract
We read with interest the article by Pinto and his colleagues, who describe a case report of coronary artery bypass grafting surgery (CABG) in a transplanted heart [1]. With increased longevity in Western countries, the number of patients with end stage heart disease is increasing and studies have reported that advanced age should not be a contraindication for heart transplantation [2]. On the other hand, this philosophical change will add to the recognized gap between available donors and recipients for heart transplantation. The use of marginal donors is therefore a potential strategy to increase donor recruitment and indeed the use of this strategy has been successful in high volume transplant centers [3–5]. We recently encountered a critical clinical situation, in which a marginal donor with significant coronary artery disease was accepted for a recipient who was in cardiogenic shock despite of maximal medical and mechanical support. A 74-year old man presented with ischaemic cardiomypathy with an ejection fraction of 13%. He had a positron emssion tomohgraphy (PET) scan, which demonstrated that he had viable hibernating myocardium in a large portion of his anterior wall, suggest-
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".