Bibliographic record
Abstract
The possible link between infection/inflammation/immune activation and a cancer patient's outcome from both a causative and outcome point of view has long been postulated. Substantial progress in the understanding of tumor-associated antigens/epitopes, immune cellular subpopulations, cytokine pathways/expression, the tumor microenvironment, and the balance between tumor-immune suppression and stimulation have been made over the past decade. This knowledge has heralded a new era of tumor immunotherapy utilizing vaccines, immune checkpoint inhibition, and oncolytic viruses. Despite significant progress in the molecular era now with targeted therapeutics such as EGFR tyrosine kinase inhibitors and ALK fusion protein inhibitors that have significantly improved the outcome of these specific lung cancer subpopulations, the overall 5 year survival for all non-small cell lung cancer (NSCLC) is still <20%. Unlike malignancies such as malignant melanoma, renal cell carcinoma, and neuroblastoma given their documented spontaneous remission rates lung cancer historically has been felt to be resistant to immune approaches likely related to an immunosuppressive tumor microenvironment and/or lack of immune recognition. Defining responding populations, understanding the mechanism(s) underlying durable immune responses, and the role of chemotherapy, radiation, oncolytic viruses, and other tumor disrupting agents in augmenting immune responses have led to improved optimization of immune therapeutic strategies. The purpose of this review is to focus on the recent advances in lung immunotherapy with an emphasis on recent clinical trials in the last 5 years in NSCLC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".