Current Immunotherapies for Lung Cancer: A Review for Respirologists
Bibliographic record
Abstract
Due to revolutionary advancements in treatment, lung cancer has had the largest improvement in mortality of all cancers over the last two decades. Despite this, it remains the type with the highest incidence and mortality of all cancers in Canada, and globally it has the second highest incidence and highest mortality. An important mechanism for cancer cell survival, and one of the hallmarks of cancer, is evasion of destruction by immune cells. Immunotherapy is a class of systemic therapy aimed at activating the cytotoxic activity of immune cells and is one of the major drivers behind the improvement in survival of patients with lung cancer (along with targeted therapies, which will not be covered in this review). Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that disrupt immunosuppressive signaling and result in increased activity of cytotoxic T cells. This article discusses the currently available ICIs and their indications in the treatment of lung cancer, immune-related toxicities and important contraindications to treatment, and a respirology-focused overview of the management of toxicities. This is not a comprehensive review of immunotherapy trials in lung cancer.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".