Thoracic organ machine perfusion: A review of concepts with a focus on reconditioning therapies
Bibliographic record
Abstract
Thoracic organ transplantation, including lung, heart, and heart-lung transplants are highly regarded as gold standard treatments for patients suffering from heart failure or chronic end stage lung conditions. The relatively high prevalence of conditions necessitating thoracic organ transplants combined with the lack of available organs has resulted in many either dying or becoming too ill to receive a transplant while on the waiting list. There is a dire need to increase both the number of organs available and the utilization of such organs. Improved preservation techniques beyond static storage have shown great potential to lengthen the current period of viability of thoracic organs while outside the body, promising better utilization rates, increased donation distance, and improved matching of donors to recipients. Ex-situ organ perfusion (ESOP) can also make some novel therapeutic strategies viable, and the combination of the ESOP platform with such reconditioning therapies endeavors to better improve functional preservation of organs in addition to making more organs viable for transplantation. Given the abundance of clinical and pre-clinical studies surrounding reconditioning of thoracic organs in combination with ESOP, we summarize in this review important concepts and research regarding thoracic organ machine perfusion in combination with reconditioning therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".