Causality Assessment for Suspected DILI During Clinical Phases of Drug Development
Bibliographic record
Abstract
Causality assessment is a critical step in establishing the diagnosis of drug induced liver injury (DILI) during drug development. DILI may resemble almost any type of liver disease, and often presents a serious challenge to clinical investigators and drug makers. The diagnosis of DILI is largely based upon a combination of a compatible clinical course, exclusion of all other reasonable causes, resemblance of clinical and pathological features to known features of liver injury due to the drug (i.e., "drug's signature"), and incidence of liver injury among patients treated with the drug compared to placebo or comparator. Causality assessment for suspected DILI is currently performed using either evaluation by physicians with expertise in liver disorders (i.e., expert opinion) or standardized scoring instruments such as the Roussel Uclaf Causality Assessment Method (RUCAM). Both approaches are widely used in the post marketing setting. Causality assessment based on expert opinion is considered superior to standardized instruments such as RUCAM, in the setting of drug development, and is currently the preferred approach during clinical trials. There is a need for a systematic revision of RUCAM that will render it more suitable for the setting of clinical trials and drug development. Careful monitoring and meticulous data collection during clinical trials are essential in all cases with established liver injury to allow for a proper causality assessment. A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials. This publication is based on the conclusions of this workshop.
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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 | 0.306 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".