A critical comparison of pharmacovigilance reporting forms in six countries with the WHO-UMC recommendations (form of the form)
Bibliographic record
Abstract
OBJECTIVES: This study will identify strengths and weaknesses of ADR reporting forms of study countries. METHODS: This was an observational study conducted at the Department of Pharmacology at Kalpana Chawla Government Medical College, Karnal. We obtained the WHO-UMC adverse event reporting guidance document for designing the ADR form for member countries. We similarly collected and analysed ADR forms from Australia, Canada, India, South Africa, the UK, and the US. Data fields were grouped into different subgroups. RESULTS: An analysis of ADR reporting forms from six countries revealed a total of 70 data fields. The US-FDA's FORM 3500 has the most fields at 50 (71 %), followed by India's CDSCO with 42 fields (60 %). According to WHO-UMC recommendations, Canada and Australia have the highest number of suggested fields at 10 (83 %). All forms were one page long except for the US-FDA's, which is five pages. CONCLUSIONS: Improving patient feedback and organisational engagement is essential to raise awareness of the reporting system. A proposed generic ADR form provides detailed information for causality assessment and could serve as a basis for a standard global reporting form.
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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 | 0.222 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".