Monitoring and Reporting Adverse Drug Reactions in India: Initiatives and Contributions from Pharmacists
Bibliographic record
Abstract
1Pharmacists have a major role in these activities and should promote the development, maintenance, and evaluation of such programs.2 Pharmacists are now being encouraged to participate and contribute to such programs in different parts of the world. In India, the concept of pharmaceutical care and pharmacists’ involvement in direct patient care is still at a preliminary stage, and the expertise of most pharmacy professionals is underutilized. The concept of “clinical pharmacy” itself is new, and health care professionals are not aware of the many patient care services that could be provided by pharmacists, including ADR monitoring and reporting. Only a few hospitals in India have a clinical pharmacist to provide patient care services. Reporting of ADRs is another aspect of medical care that is still in its infancy in India, and only a handful of hospitals have a system for ADR reporting. Pharmacists have been instrumental in initiating and coordinating such systems in many of these hospitals as part of their clinical pharmacy activities. Of late, there has been a fresh initiative from the government of India with the launch of a national pharmacovigilance program that is being operated in association with the World Health Organization pharmacovigilance program. Pharmacists have significant involvement in the national program as well, primarily
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".