Evaluation of the Reproducibility of the Naranjo Adverse Drug Reaction Probability Scale Score in Published Case Reports
Bibliographic record
Abstract
STUDY OBJECTIVE: To assess the reproducibility of Naranjo Adverse Drug Reaction Probability Scale (APS) scores in published case reports. DESIGN: Reliability analysis. MEASUREMENTS AND MAIN RESULTS: Randomly selected case reports using the APS were identified from the Web of Science database. The APS scores were blinded from the case reports, and scores were then independently calculated by four raters, using the APS. The percentage of exact agreement between raters' and the published APS scores was calculated for all case reports. Categorical scores were compared by using a weighted κ statistic. For numerical scores, descriptive statistics were computed by using raw and absolute difference scores. Twenty-four case reports were independently scored by four raters. Exact agreement between all raters' scores and the published APS scores was found in five (21%) of the 24 reports. Agreement between individual rater's scores and the published categorical score ranged from 42% to 79%. Weighted κ ranged from 0.12 to 0.61, corresponding to strengths of agreement between poor and good. Difference in scoring by raters resulted in 18% and 27% of case reports being reclassified into higher and lower than reported APS categories, respectively. CONCLUSION: Exact agreement between raters' scores and the published APS score was infrequent. We recommend that authors of case reports include all pertinent details of the case and that journals ensure the robustness of the causality assessment during peer review.
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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 | 0.278 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".