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Record W4393903690 · doi:10.34172/ijhpm.2024.8239

Phase IV Drug Trials With a Canadian Site: A Comparison of Industry and Non-Industry-Funded Trials

2024· article· en· W4393903690 on OpenAlexafffundabout
Joel Lexchin, Blue Miaoran Dong, Aravind Ramanathan, Marc‐André Gagnon

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2024
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsCarleton UniversityYork UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClinical trialPharmaceutical industryDrug industryDrug trialMedicineBusinessFamily medicineInternal medicinePharmacologyEngineering

Abstract

fetched live from OpenAlex

Recent regulatory reforms have favored expedited drug marketing and increased reliance on Phase IV clinical trials for safety and efficacy assurance. This study, utilizing ClinicalTrials.gov, assesses the characteristics of Phase IV trials, with at least one site in Canada, examing those funded by industry sponsors and those lacking industry funding. Additionally, it compares the publication status of industry-funded and non-industry-funded trials through a manual review of the medical literature. Between 2000 and 2022, 864 Phase IV trials were completed, with 480 (55.6%) receiving industry funding and 384 (44.4%) funded solely by non-industry sources. Industry-funded clinical trials were larger (mean 204 enrollees versus 70), more likely to be international (57.7% versus 9.6%) and reported results more promptly (1.21 years after completion versus 1.85 years), yet both types shared similar design, outcomes, and completion time. Publication rates were 81.8% for industry-funded and 65.8% for non-industry-funded trials. The ClinicalTrials.gov registry displayed 48 inaccuracies in publication associations, raising concerns about its accuracy. Our findings underscore the existing institutional limitations in ensuring comprehensive reporting and publication of Phase IV trial results funded by both industry and non-industry sources.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.513
Teacher spread0.381 · 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.

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

Citations0
Published2024
Admission routes3
Has abstractyes

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