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
Hepatic inflammation is a common finding during a variety of liver diseases including drug-induced liver toxicity. The inflammatory phenotype can be attributed to the innate immune response generated by Kupffer cells, monocytes, neutrophils, and lymphocytes. The adaptive immune system is also influenced by the innate immune response leading to liver damage. This review summarizes recent advances in specific mechanisms of immune-mediated hepatotoxicity and its application to drug-induced liver injury. Basic mechanisms of activation of lymphocytes, macrophages, and neutrophils and their unique mechanisms of recruitment into the liver vasculature are discussed. In particular, the role of adhesion molecules and various inflammatory mediators in this process are explored. In addition, the authors describe mechanisms of liver cell damage by these inflammatory cells and critically evaluate the functional significance of each cell type for predictive and idiosyncratic drug-induced liver injury. It is expected that continued advances in our understanding of immune mechanisms of liver injury will lead to an earlier detection of the hepatotoxic potential of drugs under development and to an earlier identification of susceptible individuals at risk for predictive and idiosyncratic drug toxicities.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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