Initial lung transplantation experience with uncontrolled donation after cardiac death in North America
Bibliographic record
Abstract
Uncontrolled donation after cardiac death (uDCD) has the potential to ameliorate the shortage of suitable lungs for transplant. To date, no lung transplant data from these donors are available from North America. We describe the successful use of these donors using a simple method of in situ lung inflation so that the organ can be protected from warm ischemic injury. Forty-four potential donors were approached, and family consent was obtained in 30 cases (68%). Of these, the lung transplant team evaluated 16 uDCDs on site, and 14 were considered for transplant pending ex vivo lung perfusion assessment. Five lungs were ultimately used for transplant (16.7% use rate from consented donors). The mean warm ischemic time was 2.8 hours. No primary graft dysfunction grade 3 was observed at 24, 48, or 72 hours after transplant. Median intensive care unit stay was 5 days (range: 2-78 days), and median hospital stay was 17 days (range: 8-100 days). The 30-day mortality was 0%. Four of 5 patients are alive at a median of 651 days (range: 121-1254 days) with preserved lung function. This study demonstrates the proof of concept and the potential for uDCD lung donation using a simple donor intervention.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".