Acute complication posttransplant: primary allograft dysfunction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Heart transplant is the gold standard treatment for patients with end-stage heart failure, improving both quality of life and survival. Despite advances in donor and recipient management, primary graft dysfunction (PGD) remains the most common cause of morbidity and mortality in the early posttransplant period. This review summarizes recent discoveries in the underlying pathophysiology, risk prediction and management of PGD. RECENT FINDINGS: The incidence of PGD appears to be rising and it is not clear whether this is due to better recognition or secular changes in transplant practice. The utilization of donation after circulatory death organs for transplant is a further consideration for the development of PGD. Organ transport systems and preservation techniques may help to prevent PGD. As some of the risk factors for developing PGD remain modifiable, we summarize the current evidence for prevention and management of PGD. SUMMARY: A better understanding will allow us to appropriately manage donors and recipients to reduce the complex interactions that lead to PGD. The development of an international consortium provides the opportunity for deep phenotyping and development of contemporary risk prediction models for PGD, which may reduce the incidence and consequent early mortality associated with heart 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".