Pneumonia after liver transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pneumonia occurs in 8-23% of patients after liver transplantation and contributes considerably to their morbidity and mortality. With the increasing acuity of liver transplantation patients in the current era, pneumonias, particularly ventilator-associated pneumonias, and multidrug-resistant pathogens, are of growing concern. RECENT FINDINGS: Postliver transplantation pneumonia cause varies with the timing of infection. In the early period (<1 month postliver transplantation), nosocomial pneumonias, including ventilator-associated pneumonias and multidrug-resistant species are most common. During the intermediate period (1-6 months postliver transplantation), opportunistic infections predominate as intensive immunosuppression persists. In the late period (>6 months postliver transplantation), community-acquired bacterial and viral pneumonias arise, as immunosuppression is reduced. Numerous risk factors have been implicated in postliver transplantation pneumonias. Prevention is aimed at reducing bacterial colonization, preventing aspiration events, and utilizing surveillance and targeted antibiotics. Novel studies have also shown reduced risk of infection with personalized immunosuppression regimens guided by an immune function assay. SUMMARY: The etiologic patterns, risk factors, and preventive measures for postliver transplantation pneumonia must be understood to minimize patient exposure to modifiable risks and optimize recipient status in the perioperative period. Prevention is multifaceted and may be enhanced by personalization of immune therapy based on predisposition to infection and graft rejection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".