Expert consensus on cancer treatment-related lung injury
Bibliographic record
Abstract
Background: Although advancements in cancer therapies have substantially improved the survival of cancer patients, these treatments may also result in acute or chronic lung injury. Cancer treatment-related lung injury (CTLI) presents with a diverse array of clinical manifestations and can involve multiple sites. Due to the lack of specific diagnostic protocols, CTLI can deteriorate rapidly and may be life-threatening if not promptly addressed. Unfortunately, there is no universally accepted consensus document on the diagnosis and management of CTLI. Methods: A multidisciplinary panel comprising experts from respiratory and critical care medicine, oncology, radiation oncology, thoracic surgery, radiology, pathology, infectious diseases, pharmacy, and rehabilitation medicine participated in this consensus development. Through a systematic literature review and detailed panel discussions, the team formulated nine key recommendations. Results: This consensus document addresses the concept, epidemiology, pathogenesis, risk factors, diagnostic approach, evaluation workflow, management strategies, differential diagnosis, type-specific management and clinical staging of CTLI. Emphasis is placed on raising awareness among clinicians and therapeutic practices through comprehensive guidelines. Conclusions: The consensus provides a detailed diagnostic protocol for CTLI and introduces a structured management framework based on grading, typing, and staging. It highlights the critical role of multidisciplinary team (MDT) collaboration and emphasizes the need for individualized, whole-process patient care strategies to optimize clinical outcomes.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".