Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent advances have led to improved outcomes in lung transplantation. The International Society for Heart and Lung Transplantation Registry data have shown a steady increase in the number of cases performed annually. Although somewhat controversial, lung transplantation (LTx) for lung cancer has also slowly increased. The current role of LTx for malignant diseases and the management challenge of incidental lung cancer in the explanted lungs are reviewed herein. RECENT FINDINGS: For a few particular scenarios (advanced multifocal bronchioloalveolar carcinoma causing chronic respiratory failure, end-stage lung disease concomitant with early stage lung cancer, and metastatic disease restricted to the lungs with the primary site controlled) in which nonsurgical alternatives fail to provide adequate palliation, LTx may be considered. Nevertheless, in order to achieve acceptable results, careful patient selection and staging are paramount. In patients with incidental bronchogenic carcinoma in the explanted lung following transplantation, the prognosis is mainly driven by the malignancy stage. SUMMARY: LTx can be performed to treat malignant diseases with results approaching those for nonneoplastic indications, given that patients are carefully selected and staged. Although they have not been widely applied in the reported lung transplant literature, modalities such as endobronchial ultrasound and positron emission tomography scan are strongly encouraged and have the potential to further refine staging in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".