Granuloma in the explanted lungs: Infectious causes and impact on post‐lung transplant mycobacterial infection
Bibliographic record
Abstract
INTRODUCTION: The significance of granuloma in explanted lungs of lung transplant recipients (LTR) on the development of post-transplant mycobacterial infection is unclear. METHODS: A retrospective review comparing LTRs and heart-lung transplant (H-LTR) recipients with granuloma in the explanted lungs between 2000 and 2012 (excluding those LTRs with granuloma due to sarcoidosis) and LTRs or H-LTRs without granuloma. Patients were followed for 2 years post-transplant. RESULTS: A total of 144 LTRs and 4 H-LTRs with granulomas (75 necrotizing and 73 non-necrotizing) and a comparator cohort of 144 LTRs and 4 H-LTRs without granuloma were analyzed. In LTRs with granulomas, identification of infectious organisms was more common by histopathology (35 AFB and 22 fungal) compared to cultures (six NTM and seven fungal) taken around time of the transplant. LTRs with granulomas were more likely to have pre-transplant non-tuberculous mycobacteria (NTM) infection compared to LTRs without granuloma; P < .01. In the multivariate analysis, having granuloma or positive mycobacterial cultures at time of transplant were associated with increased risk of post-transplant mycobacterial infection (HR = 1.8 95% CI [1.024-3.154]; P = .041 and HR = 2.083 95% CI [1.011-4.292]; P = .047). Although there was a trend toward increase mycobacterial disease in those with granulomas P = .056, there was no difference in survival post-transplantation between those with or without granuloma in the explanted lung; P = .886. CONCLUSION: The presence of granuloma in the explanted lungs of LTRs or positive mycobacterial cultures at time of transplant is associated with an increased risk of mycobacterial infection post-transplant.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".