Bibliographic record
Abstract
Non-malignant cells within the tumor microenvironment play important roles in modulating tumor progression to malignancy.Many of these cells are derived from the hematopoietic system.Particularly abundant are macrophages whose density in many different human tumor types is usually positively correlated with poor prognosis suggesting that macrophages are tumor promoting.Studies in mouse models reinforce this idea since genetic or chemical ablation of macrophages results in a reduction in tumor progression and metastasis (1) (2).Functional studies have identified several tumor-promoting functions for macrophages in primary tumors.These include promotion of angiogenesis, tumor cell invasion, migration and intravasation.In some cases the signaling molecules that are produced by macrophages have been identified at least in the context of these mouse models of breast cancer (3, 4).In addition to these effects of macrophages at the primary tumor site we have recently identified a sub-population of macrophages that are required for metastatic seeding and persistent growth at distant sites.These data together with that of others, suggest that targeting macrophages and their unique signaling pathways could offer new therapeutic strategies against metastatic disease.1.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.812 | 0.573 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".