[Problems in donor lung evaluation for transplantation with regard to airway infection].
Bibliographic record
Abstract
The shortage of donor organs has been 1 of the major obstacles to solid organ transplantation. Typical lung donor criteria include clear lung field on chest radiograph, adequate oxygenation, acceptable lung compliance, and satisfactory bronchoscopic findings. To extend usage of available donors, liberalization of donor lung selection criteria has been facilitated, however, marginal donor lungs must be used with discretion, because donor lung injury, especially that related to infection, has a potential leading to early post-operative death of the recipient. From March 2000 to December 2006, we evaluated 15 braindead donors and at least 1 of the lungs from 9 donors was judged suitable for transplantation. One of 9 recipients developed severe pneumonia cased by carbapenems-resistant Pseudomonas aeruginosa possibly originating from the donor lungs, eventually leading to death. The chest radiograph and oxygenation of the donor had been satisfactory, however, a moderate amount of mucopurulent secretions was observed by bronchoscopic inspection and the donor had been given a cefozopran for 9 days before the procurement operation. Remaining 8 recipients were free from air-way infection in the early postoperative period. We discuss the status and problems of donor lung evaluation for transplantation with regard to donor lung infection.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".