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Record W3183377166 · doi:10.18433/jpps32138

A Legislative/Legal History of Prescription Drug Advertising and Promotion Regulation

2021· review· en· W3183377166 on OpenAlexvenueno aff
Stephen Li, Iris C. Gibbs

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2021
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)AttendancePublic relationsLegislatureMedical prescriptionBusinessHealth careAdvertisingPrescription drugMedicinePolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

PURPOSE: The communication by pharmaceutical companies of promotional messages about their products has long been controversial, but deemed to be necessary by the pharmaceutical industry so that health care professionals and in some cases patients/consumers can be made aware of the latest developments through the communication vehicles they are accustomed to seeing - in the case of health care professionals, through medical advertising, direct mail, visits by company representatives, and attendance at medical meetings, and in case of patients, through the news media and television advertising. On the other hand, critics argue that such promotion, which sometimes reduces complex medical issues to advertising slogans, is inappropriate for products intended to treat and cure diseases, and that health care professionals should learn about new products from peer-reviewed medical literature. Consequently, advertising, and promotional programs are heavily regulated by the U.S. Food and Drug Administration (FDA). However, the laws themselves raise constitutional issues of infringement on free speech. Over the past few years, a number of lawsuits have been decided that help clarify the role of the FDA and the extent of its authority in regulating what companies or their employees say about their products. These court decisions are important because they help define how health care professionals and patients/consumers receive medical information. METHODS: This overview is intended to identify, in non-technical language, some of the more controversial and challenging issues involved in the FDA's efforts to regulate marketing communications by drug companies and how the courts view them. RESULTS: The recent lawsuits often involve complex and far-reaching legal issues. But when examined in toto, as this paper does, they have reflected a view by the courts that truthful and non-misleading statements by drug companies about their products can be legally communicated even when the medical information is not formally approved by the FDA and included in the FDA-approved labeling. The lawsuits thus have led to an environment in which the FDA continues to oversee with great fervor the activities of drug companies in communicating medical information but at the same time having some flexibility in keeping health care professionals and patients up to date with th latest information about medical research and new therapeutic products. CONCLUSION: How pharmaceutical products are marketed has been deemed by the U.S. Congress to be important enough to need to be subject to federal regulation. The issues create a tension between the need for medical information to be accurate and balanced, and the guarantees of free speech. This review provides an important perspective on how this tension is being resolved, even as dramatic advances in both medical products and technology create new challenges.

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)0.015
metaresearch head score (Gemma)0.020
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 score0.016
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0090.025
Scholarly communication0.0120.009
Open science0.0020.004
Research integrity0.0140.019
Insufficient payload (model declined to judge)0.0050.001

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 head0.605
GPT teacher head0.598
Teacher spread0.007 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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