Bibliographic record
Abstract
Epidemiological evidence is regularly presented to courts in determining proof of causation in medicinal product liability litigation. Building on the foundations of the author’s previous monograph, which supported the use of epidemiological evidence in dealing with problems of proof of causation in alleged cases of adverse drug reactions, this paper revisits this perennial problem of the role of epidemiological evidence in assessing causation in product liability cases in a twenty-first century context, examining recent cases in the United Kingdom, United States, Australia, and Canada. It seeks to determine the extent to which the courts in the highlighted cases have been pragmatic and fair in their interpretation and utilization of epidemiological evidence, from the perspective of both consumers and pharmaceutical manufacturers. The paper examines the apparent tension between the levels of proof required in law and science, including the relationship between levels of statistical significance and the claimant’s burden of proof; and it assesses the wisdom of using a doubling of the risk rule as a threshold to any recovery. It explores the ways in which probabilistic methods, including statistical refining with individual risk factors, can be used in conjunction with epidemiological evidence to determine specific causation. The paper supports the view that logistic regression techniques and other forms of statistical refining mechanisms using specific risk factors can and do help in the process of giving quantitative or quasi-quantitative expression to conclusions about the cause of disease in an individual drug product liability claim that is based on epidemiological evidence.
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 | 0.062 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".