Assessment of infection risks prior to lung transplantation
Bibliographic record
Abstract
PURPOSE OF THIS REVIEW: Infections are major causes of morbidity and mortality after lung transplantation. Pretransplant evaluation can identify patients at risk of infectious complications and guide prophylactic strategies post transplantation. This review focuses on studies published from 2006 to the present that relate to the assessment of risk of infection prior to lung transplantation. RECENT FINDINGS: Pretransplant airways colonization with Pseudomonas, Burkholderia, nontuberculosis mycobacteria, Aspergillus and Scedosporium tend to recur after transplantation and cause disease in the lung allograft. Recently, colonization with Pseudomonas and Aspergillus species has been implicated in the subsequent development of allograft dysfunction. B. cenocepacia and Mycobacterium abscessus are particularly associated with poor outcomes after lung transplantation and are considered to be relative contra-indications to lung transplantation in many centers. Tuberculin skin test (TST) has limited value in predicting tuberculosis (TB) reactivation; however, in the absence of a better test, it remains the gold standard for screening patients with latent TB. Serologic screening for histoplasmosis and toxoplasmosis has limited value as these infections rarely occur after lung transplantation. SUMMARY: Recurrence of pretransplant airway infection and reactivation of latent infection are potential sources of infection after lung transplantation. Prospective studies are needed to determine the efficacy of prophylactic antimicrobial strategies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".