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
Glioblastoma is an aggressive and incurable brain cancer. This cancer establishes both local and systemic immunosuppression that creates a major obstacle to effective immunotherapies. Many studies point to tumor-resident myeloid cells (primarily microglia and macrophages) as key mediators of this immunosuppression. Myeloid cells exhibit a high level of plasticity with respect to their phenotype and are capable of both stimulating and repressing immune responses. How glioblastomas recruit myeloid cells and exploit them to avoid the immune system is an active area of research. Macrophages can acquire an immunosuppressive phenotype as a consequence of exposure to cytokines such as TGFB1 or IL4; in addition, macrophages can acquire an immunosuppressive phenotype as a consequence of the engulfment of apoptotic cells, a process referred to as efferocytosis. There is substantial evidence that glioblastoma cells are able to secrete cytokines and other factors that induce an immunosuppressive phenotype in macrophages and microglia. However, less is known about the contribution of efferocytosis to immunosuppression in glioblastoma. Here I review the literature in this area and discuss the potential of efferocytosis inhibition to improve glioblastoma response to immunotherapy.
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 | 0.000 |
| 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 | 0.000 |
| 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