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Record W2912300910 · doi:10.1371/journal.pone.0212399

Association between commercial funding of Canadian patient groups and their views about funding of medicines: An observational study

2019· article· en· W2912300910 on OpenAlexafffundabout
Joel Lexchin

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsYork UniversityUniversity of TorontoUniversity Health Network
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchAgency for Healthcare Research and QualityGovernment of CanadaMedical Research CouncilGordon and Betty Moore Foundation
KeywordsConflict of interestObservational studyFamily medicineMedicineDrugProduct (mathematics)Public relationsPolitical scienceBusinessPharmacologyFinanceInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patient groups represent the interest of their members when it comes to drug funding. Many patient groups receive grants from pharmaceutical companies that make products being considered for funding. This research examines whether there is an association between the positions that Canadian groups take about the products and conflicts of interest with the companies. METHODS: The Common Drug Review (CDR) and panCanadian Oncology Drug Review (pCODR) make recommendations to Canadian provincial and federal drug plans about funding particular drug-indications. Both utilize input from patient groups in making their recommendations. Patient group submissions are available from both organizations and these submissions contain statements about conflicts of interest. Views of the patient groups, with and without a conflict with the company making the drug under consideration and without any conflicts at all, were assessed and then compared with the recommendations from CDR and pCODR. RESULTS: There was a total of 222 reports for drug-indications. There were 372 submissions from 93 different patient groups. Groups declared a total of 1896 conflicts with drug companies in 324 (87.1%) individual submissions. There were 268 submissions where groups declared a conflict with the company making the product or said they had no conflict. Irrespective of whether there was a conflict, the views of patient groups about the drug-indications under consideration were the same. There was no statistically significant difference between views of patient groups and the recommendations from CDR and/or pCODR. CONCLUSIONS: The large majority of patient groups making submissions about funding of particular drug-indications had conflicts with the companies making the products and their views about the products were almost always positive. This association between funding and views needs to be further investigated to determine if a true cause and effect exists.

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)0.006
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.856

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.798
GPT teacher head0.526
Teacher spread0.272 · 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.

Study designObservational
DomainIncentives
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

Citations24
Published2019
Admission routes3
Has abstractyes

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