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Record W1953028498 · doi:10.1002/pds.3859

Benefit–Risk Assessment, Communication, and Evaluation (BRACE) throughout the life cycle of therapeutic products: overall perspective and role of the pharmacoepidemiologist

2015· review· en· W1953028498 on OpenAlexaff
Christine Radawski, Elaine H. Morrato, Kenneth Hornbuckle, Priya Bahri, Meredith Y. Smith, Juhaeri Juhaeri, Peter G. M. Mol, Bennett Levitan, Hanyao Huang, Paul Coplan, Hu Li

Bibliographic record

VenuePharmacoepidemiology and Drug Safety · 2015
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPurdue Pharma (Canada)
FundersInternational Society for Pharmacoepidemiology
KeywordsMedicineRisk analysis (engineering)Risk assessmentProcess (computing)BraceProduct (mathematics)PopulationEngineeringComputer scienceEnvironmental healthComputer security

Abstract

fetched live from OpenAlex

PURPOSE: Optimizing a therapeutic product's benefit-risk profile is an on-going process throughout the product's life cycle. Different, yet related, benefit-risk assessment strategies and frameworks are being developed by various regulatory agencies, industry groups, and stakeholders. This paper summarizes current best practices and discusses the role of the pharmacoepidemiologist in these activities, taking a life-cycle approach to integrated Benefit-Risk Assessment, Communication, and Evaluation (BRACE). METHODS: A review of the medical and regulatory literature was performed for the following steps involved in therapeutic benefit-risk optimization: benefit-risk evidence generation; data integration and analysis; decision making; regulatory and policy decision making; benefit-risk communication and risk minimization; and evaluation. Feedback from International Society for Pharmacoepidemiology members was solicited on the role of the pharmacoepidemiologist. The case example of natalizumab is provided to illustrate the cyclic nature of the benefit-risk optimization process. RESULTS: No single, globally adopted benefit-risk assessment process exists. The BRACE heuristic offers a way to clarify research needs and to promote best practices in a cyclic and integrated manner and highlight the critical importance of cross-disciplinary input. Its approach focuses on the integration of BRACE activities for risk minimization and optimization of the benefit-risk profile. CONCLUSION: The activities defined in the BRACE heuristic contribute to the optimization of the benefit-risk profile of therapeutic products in the clinical world at both the patient and population health level. With interdisciplinary collaboration, pharmacoepidemiologists are well suited for bringing in methodology expertise, relevant research, and public health perspectives into the BRACE process.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.150
metaresearch head score (Gemma)0.149
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.150
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.149
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0090.005
Science and technology studies0.0030.011
Scholarly communication0.0190.013
Open science0.0040.009
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.364
GPT teacher head0.544
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2015
Admission routes1
Has abstractyes

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