<i>In vitro</i>testing for diagnosis of idiosyncratic adverse drug reactions: Implications for pathophysiology
Bibliographic record
Abstract
Idiosyncratic drug reactions (IDRs) represent a major health problem, as they are unpredictable, often severe and can be life threatening. The low incidence of IDRs makes their detection during drug development stages very difficult causing many post-marketing drug withdrawals and black box warnings. The fact that IDRs are always not predictable based on the drug's known pharmacology and have no clear dose-effect relationship with the culprit drug renders diagnosis of IDRs very challenging, if not impossible, without the aid of a reliable diagnostic test. The drug provocation test (DPT) is considered the gold standard for diagnosis of IDRs but it is not always safe to perform on patients. In vitro tests have the advantage of bearing no potential harm to patients. However, available in vitro tests are not commonly used clinically because of lack of validation and their complex and expensive procedures. This review discusses the current role of in vitro diagnostic testing for diagnosis of IDRs and gives a brief account of their technical and mechanistic aspects. Advantages, disadvantages and major challenges that prevent these tests from becoming mainstream diagnostic tools are also discussed here.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.006 | 0.004 |
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".