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Record W2734614665 · doi:10.1016/j.jsps.2017.07.006

Comparison of the safety information on drug labels in three developed countries: The USA, UK and Canada

2017· article· en· W2734614665 on OpenAlexaboutno aff
Thamir M. Alshammari, Venkat Ratnam Devadasu, R.P. Rathnam

Bibliographic record

VenueSaudi Pharmaceutical Journal · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)MedicineBusinessDrugMarketingComputer sciencePharmacology

Abstract

fetched live from OpenAlex

The safety information on drug labels of a company marketing the same drugs in different countries is sometimes different. The aim of the present study is to understand the differences in the volume and content of safety information on the drug labels from the same manufacturers in three developed countries: the United States of America (USA), the United Kingdom (UK) and Canada. This study involved the calculation of the proportion of total safety information (PSI) and of contraindications (PCI) in comparison to all information on the label and the percentage of boxed warnings (PBW) among the 100 labels studied from each country. The PSI on the labels of different countries is different with USA labels bearing lesser value PSI and UK labels bearing higher value PSI. The qualitative information provided on these drug labels from each country in 'contraindications' sections, 'boxed/serious warnings' and 'overdosage' sections presented differences in the information provided on most of the labels. We have found distinct differences between the safety information available on drug labels in terms of volume and content. We conclude that the safety information for the same products should be standardised across all countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.128
GPT teacher head0.463
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2017
Admission routes1
Has abstractyes

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