An introduction to adverse drug reaction reporting systems in different countries
Bibliographic record
Abstract
Abstract Objective To review adverse drug reaction (ADR) reporting schemes in selected developed countries, with emphasis on identifying community pharmacists' roles in ADR reporting. Setting International comparison between eight developed countries, with respect to ADR reporting systems and developments. Method Review of published articles on ADR reporting by pharmacists. Health and medical sciences databases including International Pharmaceutical Abstracts, MEDLINE and ProQuest were searched for relevant publications from 1993 to 2003. Websites specific to ADR reporting schemes in the selected countries were also searched. Key findings ADRs impact significantly on a nation's healthcare costs. Voluntary reporting by health professionals is currently considered the cornerstone to the detection and management of ADRs and makes a valuable contribution to the safe use of medicines. ADR reporting systems are managed by national ADR or pharmacovigilance reporting centres, and differ internationally. In general, medication-related problems are reported more commonly in hospitals than in the community. Physicians are the main contributors, except in the Netherlands and Canada, where community pharmacists play the major role in ADR reporting. Time pressure, no remuneration for reporting, and confusion about what to report were identified as some of the main deterrents for reporting by pharmacists. Conclusion Most international reporting systems for ADRs are either hospital based, or physician based. The opportunity therefore exists to further develop reporting systems that are accessible by community pharmacists, as they are in an ideal situation to detect and report ADRs through contact with patients.
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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 | 0.049 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.042 | 0.044 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".