Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of patients listed for lung transplantation largely exceeds the number of available transplantable organs because of both a shortage of organ donors and a low utilization rate of lungs from those donors. A novel strategy of donor lung management, ex-vivo lung perfusion (EVLP), that keeps the organ at physiological protective conditions has shown great promise to increase lung utilization by re-evaluating, treating, and repairing donor lungs prior to transplantation. RECENT FINDINGS: Clinical trials using EVLP have shown the method to be well tolerated and it allows for reassessment and improvement in function from high-risk donor lungs from both brain death and cardiac death donors prior to transplantation. When these lungs were transplanted, low rates of primary graft dysfunction were achieved, and the early outcomes were similar to those with conventionally selected and transplanted lungs. Preclinical studies have also shown a great potential of EVLP as a platform for the delivery of novel therapies to repair injured organs ex vivo and thus further increase the donor lung utilization rate. SUMMARY: Development of an ex-vivo treatment arsenal ranging in complexity from pharmacologic to gene and cellular therapies will soon allow clinicians to utilize the full potential of the donor organ pool improving outcomes of lung transplantation.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".