Macrophages, Inflammation, and Lung Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer mortality worldwide, and at only 18%, it has one of the lowest 5-year survival rates of all malignancies. With its highly complex mutational landscape, treatment strategies against lung cancer have proved largely ineffective. However with the recent success of immunotherapy trials in lung cancer, there is renewed enthusiasm in targeting the immune component of tumors. Macrophages make up the majority of the immune infiltrate in tumors and are a key cell type linking inflammation and cancer. Although the mechanisms through which inflammation promotes cancer are not fully understood, two connected hypotheses have emerged: an intrinsic pathway, driven by genetic alterations that lead to neoplasia and inflammation, and an extrinsic pathway, driven by inflammatory conditions that increase cancer risk. Here, we discuss the contribution of macrophages to these pathways and subsequently their roles in established tumors. We highlight studies investigating the association of macrophages with lung cancer prognosis and discuss emerging therapeutic strategies for targeting macrophages in the tumor microenvironment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it