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Record W2472949758 · doi:10.1371/journal.pmed.1002046

Agreements between Industry and Academia on Publication Rights: A Retrospective Study of Protocols and Publications of Randomized Clinical Trials

2016· article· en· W2472949758 on OpenAlexafffund
Benjamin Kasenda, Erik von Elm, John J. You, Anette Blümle, Yuki Tomonaga, Ramon Saccilotto, Alain Amstutz, Theresa Bengough, Joerg J Meerpohl, Mihaela Stegert, Kelechi Kalu Olu, Kari A.O. Tikkinen, Ignacio Neumann, Alonso Carrasco‐Labra, Markus Faulhaber, Sohail Mulla, Dominik Mertz, Elie A. Akl, Dirk Bassler, Jason W. Busse, Ignacio Ferreira‐González, François Lamontagne, Alain Nordmann, Viktoria Gloy, Heike Raatz, Lorenzo Moja, Shanil Ebrahim, Stefan Schandelmaier, Xin Sun, Per Olav Vandvik, Bradley C. Johnston, Martin A. Walter, Bernard Burnand, Matthias Schwenkglenks, Lars G. Hemkens, Heiner C. Bucher, Gordon Guyatt, Matthias Briel

Bibliographic record

VenuePLoS Medicine · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsHospital for Sick ChildrenUniversité de SherbrookeUniversity of TorontoMcMaster University
FundersHamilton Health Sciences FoundationGottfried und Julia Bangerter-Rhyner-StiftungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche ForschungsgemeinschaftCanadian Institutes of Health ResearchSuomen KulttuurirahastoSuomen Lääketieteen SäätiöSichuan UniversityHamilton Health SciencesFondation BrocherNational Science Foundation
KeywordsRandomized controlled trialMedicineProtocol (science)MEDLINEDocumentationClinical trialFamily medicineConsolidated Standards of Reporting TrialsConsistency (knowledge bases)Publication biasAlternative medicineMeta-analysisPolitical scienceSurgeryInternal medicineLawPathologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about publication agreements between industry and academic investigators in trial protocols and the consistency of these agreements with corresponding statements in publications. We aimed to investigate (i) the existence and types of publication agreements in trial protocols, (ii) the completeness and consistency of the reporting of these agreements in subsequent publications, and (iii) the frequency of co-authorship by industry employees. METHODS AND FINDINGS: We used a retrospective cohort of randomized clinical trials (RCTs) based on archived protocols approved by six research ethics committees between 13 January 2000 and 25 November 2003. Only RCTs with industry involvement were eligible. We investigated the documentation of publication agreements in RCT protocols and statements in corresponding journal publications. Of 647 eligible RCT protocols, 456 (70.5%) mentioned an agreement regarding publication of results. Of these 456, 393 (86.2%) documented an industry partner's right to disapprove or at least review proposed manuscripts; 39 (8.6%) agreements were without constraints of publication. The remaining 24 (5.3%) protocols referred to separate agreement documents not accessible to us. Of those 432 protocols with an accessible publication agreement, 268 (62.0%) trials were published. Most agreements documented in the protocol were not reported in the subsequent publication (197/268 [73.5%]). Of 71 agreements reported in publications, 52 (73.2%) were concordant with those documented in the protocol. In 14 of 37 (37.8%) publications in which statements suggested unrestricted publication rights, at least one co-author was an industry employee. In 25 protocol-publication pairs, author statements in publications suggested no constraints, but 18 corresponding protocols documented restricting agreements. CONCLUSIONS: Publication agreements constraining academic authors' independence are common. Journal articles seldom report on publication agreements, and, if they do, statements can be discrepant with the trial protocol.

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)0.019
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.783
GPT teacher head0.679
Teacher spread0.103 · 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; both teacher heads agree on what is shown here.

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

Citations35
Published2016
Admission routes2
Has abstractyes

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