The Price of Success: Immune-Related Adverse Events from Immunotherapy in Lung Cancer
Bibliographic record
Abstract
Cancer immunotherapy has the goal of enhancing a patient's intrinsic immune processes in order to mount a successful immune response against tumor cells. Cancer cells actively employ tactics to evade, delay, alter, or attenuate the anti-tumor immune response. Immune checkpoint inhibitors (ICIs) modulate endogenous regulatory immune mechanisms to enhance immune system activation, and have become the mainstay of therapy in many cancer types. This activation occurs broadly and as a result, activation is supraphysiologic and relatively non-specific, which can lead to immune-related adverse events (irAEs), the frequency of which depends on the patient, the cancer type, and the specific ICI antibody. Careful assessment of patients for irAEs through history taking, physical exam, and routine laboratory assessments are key to identifying irAEs at early stages, when they can potentially be managed more easily and before progressing to higher grades or more serious effects. Generally, most patients with low grade irAEs are eligible for re-challenge with ICIs, and the use of corticosteroids to address an irAE is not associated with poorer patient outcomes. This paper reviews immune checkpoint inhibitors (ICIs) including their mechanisms of action, usage, associated irAEs, and their management.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".