ADR in Journals: Are They Translated into Regulatory Frameworks?
Bibliographic record
Abstract
INTRODUCTION: An adverse drug reaction case report refers to a scientific publication that is written by a health care professional who suspects a casual relationship between a drug and an adverse drug reaction (ADR). ADR case reports help to identify potential risks associated with the use of drug. Most of the case reports do not mention about reporting the ADR to regulatory authorities. With this objective, the aim of this study was to analyze the number of Adverse Drug Reactions (ADR) published as case reports (PubMed indexed journals) from January 2018 to June 2019, and observe if they are translated in regulatory frameworks like Vigibase, and package inserts. MATERIALS AND METHODS: 321 ADRs were obtained with the keywords "Adverse Drug Reaction". Out of those, 158 were independently extracted by two investigators, observed and categorized according to classes of the drugs, geographic location, severity, hospitalization, Completeness of ADR, whether reported to the regulatory authority (Vigibase), or listed in the package insert. Literature review articles were excluded. RESULTS: Out of the 158 ADRs, antibiotics accounted for 12.65%, CNS drugs and monoclonal antibodies11.39%, anticancer drugs 9.49%, CVS drugs 4.43%, anti-viral 3.79%, others 45.56%, respectively. According to geographic region, 26 ADRs published were from USA, Australia 4, Italy 3, India 17, Turkey 9, Singapore and UK 1, China 20, Denmark and Canada 2, Japan 10, France 9, Austria 1, Korea 5, South America 3, Switzerland 2, respectively. Depending upon the severity, causality assessment was done only for 45 ADRs, and not done for 113 ADRs. 41.13% patients (from 65 case reports) were hospitalized. Among the 158 ADRs, 14 ADRs were not found in Vigibase. 32 ADRs were not mentioned in the Drug package inserts. When categorized according to the completeness of case reports, weight accounted for1.89%, lab values and procedure for diagnosis, 96.8%, risk factors, 95.56%, prior exposure, 88.60%, Post ADR status, 60.12%, start-stop medication, route of administration, first dose, last dose, duration of illness accounted for 100%, respectively. CONCLUSION: Depending upon our observation, we have noticed that there is deficiency in reporting of suspected ADRs to regulatory authorities. Reporting can be included as mandatory criteria for ADR case reports. Also, there is an increased need to aware various healthcare workers for reporting ADR.
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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 | 0.045 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.040 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".