Balancing Innovation and Safety: Prediction, Prevention, and Management of Pneumonitis in Lung Cancer Patients Receiving Novel Anti-Cancer Agents
Bibliographic record
Abstract
Pneumonitis is characterized as inflammation of the lung parenchyma, and a potential adverse effect of several anti-cancer therapies. Diagnosing pneumonitis can be particularly challenging in lung cancer patients due to inherent similarities in symptoms and radiological presentation associated with pneumonitis, as well as other common conditions such as infection or disease progression. Furthermore, many lung cancer patients have underlying pulmonary conditions that might render them more susceptible to severe or fatal outcomes from pneumonitis. Novel anti-cancer agents, such as antibody-drug conjugates (ADCs) and bispecific antibodies (BsAbs), are being incorporated into the treatment of lung cancer; therefore, understanding the risk and mechanisms underlying the potential development of pneumonitis with these new therapies is important to ensure continuous improvements in patient care. This narrative review provides an overview of the incidence of pneumonitis observed with novel anti-cancer agents, characterizes potential pathophysiological mechanisms underlying pneumonitis risk and emerging predictive biomarkers, highlights management strategies, and explores future directions for minimizing the risk of pneumonitis for lung cancer patients.
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) | 0.001 | 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 | 0.001 |
| 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".