Bibliographic record
Abstract
Although the major working hypothesis for the mechanism of idiosyncratic drug reactions (IDRs), the hapten hypothesis, has not changed since 1987, several hypotheses have been added, for example, the danger hypothesis and the pharmaceutical interaction hypothesis. Genetic studies have found that several IDRs are linked to specific HLA genes, providing additional evidence that they are immune-mediated. Evidence that most IDRs are caused by reactive metabolites has led pharmaceutical companies to avoid drug candidates that form significant amounts of reactive metabolites; however, at least one IDR, ximelagatran-induced liver toxicity, does not appear to be caused by a reactive metabolite. It is possible that there are biomarkers such as those related to cell stress that would predict that a drug candidate would cause a significant incidence of IDRs; however, there has been no systematic study of the changes in gene expression induced by drugs known to cause IDRs. A major impediment to the study of the mechanisms of IDRs is the paucity of valid animal models, and if we had a better mechanistic understanding, it should be easier to develop such models. There is growing evidence that these adverse reactions are more varied and complex than previously recognized, and it is unlikely that a quick fix will be achieved. However, IDRs are an important cause of patient morbidity and mortality and markedly increase the uncertainty of drug development; therefore, continued basic research in this area is essential.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".