Immune-mediated respiratory adverse events of checkpoint inhibitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Immune checkpoint inhibitors have demonstrated remarkable efficacy with durable responses in the treatment of various malignancies. This new class of therapeutic agents is associated with a toxicity profile that differs from conventional cytotoxic therapy. The present review is focused on one of these toxicities affecting the respiratory system. RECENT FINDINGS: Many types of immune-related adverse events (irAEs) have been identified since the emergence of checkpoint inhibitors including colitis, nephritis, myasthenia gravis-like syndromes, acute interstitial nephritis, pneumonitis, and endocrinopathies. Although pneumonitis is relatively less frequent than other irAEs, this toxicity is by no means inconsequential as it has led to treatment-related deaths during the initial testing phases. SUMMARY: Immune-mediated pneumonitis is a potentially serious but relatively infrequent adverse event associated with the use of immune checkpoint inhibitors. IrAEs can be challenging for oncologists who are still unfamiliar with the early presenting symptoms and subsequent management of these toxicities, especially in the context of a rapidly expanding science. A high index of suspicion for pneumonitis must be maintained in patients receiving checkpoint inhibitors and who present new onset respiratory symptoms because this type of toxicity can be severe and potentially fatal.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".