Ischemia–Reperfusion–induced Lung Injury
Bibliographic record
Abstract
Ischemia-reperfusion-induced lung injury is characterized by nonspecific alveolar damage, lung edema, and hypoxemia occurring within 72 hours after lung transplantation. The most severe form may lead to primary graft failure and remains a significant cause of morbidity and mortality after lung transplantation. Over the past decade, better understanding of the mechanisms of ischemia-reperfusion injury, improvements in the technique of lung preservation, and the development of a new preservation solution specifically for the lung have been associated with a reduction in the incidence of primary graft failure from approximately 30 to 15% or less. Several strategies have also been introduced into clinical practice for the prevention and treatment of ischemia-reperfusion-induced lung injury with various degrees of success. However, only three randomized, double-blinded, placebo-controlled trials on ischemia-reperfusion-induced lung injury have been reported in the literature. In the future, the development of new agents and their application in prospective clinical trials are to be expected to prevent the occurrence of this potentially devastating complication and to further improve the success of lung transplantation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".