Comparative evaluation of adverse drug reaction reporting forms for introduction of a spontaneous generic ADR form
Bibliographic record
Abstract
Despite comprehensive and stringent phases of clinical trials and surveillance efforts, unexpected and serious adverse drug reactions (ADRs) repeatedly occur after the drug is marketed. ADR reporting is an important aspect of an efficient and effective pharmacovigilance program. Although Medwatch, Yellow Card, CDSCO form, etc. are the protocol forms of ADR collection and reports, a number of countries design and use their respective ADR forms. This review compares similarities and dissimilarities of 13 ADR forms of countries representing their geographical location. This study extracted 73 data elements mentioned in 13 different ADR forms. Only 13 elements were common. An ADR form of Malaysia and Canada covers the highest number of data 43, while Brazil falls to the opposite end with a number of 17 data elements in lieu with the Generic ADR Form. The result of this review highlights 58 data elements of the proposed generic ADR form which ensures that requisite reporting information essential for correct causality assessment of ADRs are included. The proposed "Generic ADR form" could be adopted worldwide mandatorily for reporting any/all ADRs associated with marketed drugs.
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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 | 0.100 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".