MétaCan
Menu
Back to cohort
Record W2053341861 · doi:10.1509/jppm.21.2.194.17595

Direct-to-Consumer Advertising of Prescription Drugs: The Evidence Says No

2002· article· en· W2053341861 on OpenAlexaff
Joel Lexchin, Barbara Mintzes

Bibliographic record

VenueJournal of Public Policy & Marketing · 2002
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsDirect-to-consumer advertisingMedical prescriptionAdvertisingBusinessMarketingMedicinePharmacology

Abstract

fetched live from OpenAlex

There is little rationale for direct-to-consumer advertising of prescription drugs. Most new drugs offer little if any therapeutic advantage over existing products. Direct-to-consumer advertisements frequently downplay safety information. Physicians are highly ambivalent about prescribing advertised drugs requested by patients. There is no evidence that direct-to-consumer advertising results in any improvement in health outcomes.

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)0.029
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.145
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0030.004
Science and technology studies0.0020.007
Scholarly communication0.0060.007
Open science0.0030.002
Research integrity0.0170.012
Insufficient payload (model declined to judge)0.0400.006

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 head0.413
GPT teacher head0.520
Teacher spread0.107 · 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 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

Citations127
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Public Policy & MarketingSame topicPharmaceutical industry and healthcareFrench-language works237,207