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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".