Bibliographic record
Abstract
Inflammation is a primary driver of cancer initiation and progression. However, the complex and dynamic nature of an inflammatory response make this a very difficult process to study. Organoids are a new model system where complex multicellular structures of primary cells can be grown in a 3D matrix to recapitulate the biology of the parent tissue. This experimental model offers several distinct advantages over alternatives including the ability to be genetically engineered, implanted in vivo and reliably derived from a wide variety of normal and cancerous tissue from patients. Furthermore, long-term organoid cultures reproduce many features of their source tissue, including genetic and epigenetic alterations and drug sensitivity. Perhaps most significantly, cancer organoids can be cocultured in a variety of different systems with a patients’ own immune cells, uniquely permitting the study of autologous cancer-immune cell interactions. Experiments with such systems promise to shed light on the mechanisms governing inflammation-associated cancer while also providing prognostic information on an individual patient’s responsiveness to immunotherapeutic anti-cancer drugs. Thanks to their ability to capture important features of the complex relationship between a cancer and its microenvironment, organoids are poised to become an essential tool for unraveling the mechanisms by which inflammation promotes 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".