Trasplante pulmonar ex vivo . Primera experiencia en Chile y Latinoamérica
Bibliographic record
Abstract
Background: The number of patients waiting for a lung transplant worldwide greatly exceeds the number of available donors. Ex vivo lung perfusion is a useful tool that allows marginal donor lungs to be evaluated and reconditioned for a successful lung transplantation. Aim: To describe the first Chilean and Latin American experience in ex vivo lung perfusion for marginal donor lungs before transplantation. Material and Methods: Descriptive analysis of all ex vivo lung perfusion conducted for marginal donor lungs at a private clinic, from April 2019 to October 2020. High risk donor lungs and rejected lungs from other transplantation centers were included. The “Toronto Protocol” was used for ex vivo lung perfusion. Donor lung characteristics and recipient outcomes were studied. Results: During the study period, five ex vivo lung perfusions were performed. All lungs were reconditioned and transplanted. No complications were associated. There were no primary graft dysfunctions and only one chronic allograft dysfunction. There was no mortality during the first year. The median arterial oxygen partial pressure/fractional inspired oxygen ratio increased from 266 mm Hg in the donor lung to 419 after 3 hours of ex vivo lung perfusion (p = 0.043). Conclusions: ex vivo lung perfusion is a safe and useful tool that allows marginal donor lungs to be reconditioned and successfully transplanted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".