Comparison of the Liverpool Causality Assessment Tool versus the Naranjo Scale for Predicting the Likelihood of an Adverse Drug Reaction
Bibliographic record
Abstract
Abstract Objective To compare the Liverpool Causality Assessment Tool versus Naranjo Scale for screening suspected adverse drug reaction (ADR) cases. Methods We retrospectively reviewed patient charts with a history of suspected ADR, scored using both instruments and determined how each correlates with laboratory and other investigations. 924 charts from the Clinical Pharmacology Clinic at the London Health Sciences Centre were reviewed and 529 charts contained objective findings to support or against the diagnosis of ADR. The participant age ranged from 1 month old to 93 years. We determined the sensitivity and specificity of Liverpool and Naranjo tools for predicting ADRs with scores ranging from “Possible” to “Definite” were considered positive and “Unlikely/Doubtful” as negative for ADR. These results were confirmed by laboratory or clinical (re-challenge) testing in 529 cases. Results Liverpool causality tool had sensitivity (SN) of 97.2% ± 2.4% and specificity (SP) of 2.3% ± 1.57%. The positive (PPV) and negative predictive values (NPV) were 34.1% and 61.5%, respectively. The Naranjo scale had SN of 81.2% ± 5.69% and SP of 13.2% ± 3.56%. PPV and NPV were 32.7% and 57.5%, respectively. Conclusions The Liverpool Causality Assessment Tool is a more sensitive tool than the Naranjo Scale in the assessment of possible ADRs but both tools have poor specificity. The Liverpool Tool can be a useful screening tool in settings where other tests may not be readily available. However the low PPV and NPV of both instruments suggests pursue further testing is needed to confirm or deny an ADR.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".