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Source of funding, conflict of interest (COI), and the interpretation of cancer clinical trials

2007· article· en· W2192442514 on OpenAlexaff
Rachel P. Riechelmann, Vera Dounaevskaia, Nathan Taback, Aoife O'Carroll, Monika K. Krzyzanowska

Bibliographic record

VenueJournal of Clinical Oncology · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity of Ottawa
Fundersnot available
KeywordsMedicineLogistic regressionFamily medicineRandomized controlled trialClinical trialClinical endpointCancerInternal medicine

Abstract

fetched live from OpenAlex

6530 Background: Concern exists that industry sponsorship and financial relationships between investigators and drug companies may bias clinical cancer research. Our objective was to determine whether funding or authors' COI are associated with interpreting cancer clinical trials in more positive light. Methods: We reviewed phase II and randomized clinical trials (RCT) of anticancer and supportive care drugs published in 5 clinical cancer journals in a one-year period. We collected information on study design, source of funding, COI disclosure and results of primary endpoints (EP). Each concluding statement in the articles′s abstracts were independently rated by two reviewers (blinded to other study information) with respect to level of enthusiasm for the experimental agent using a 5-point scale ( Table 1 ). Summary statistics and logistic regression were used to describe the results. Results: 213 articles met inclusion criteria: 124 phase II and 89 RCT. Approximately 40% were funded by industry, at least one COI was declared in 35% of articles. Among 130/213 (61%) articles with clearly positive conclusions, the proportion of articles with highly positive conclusions was 61% in articles that declared COI vs. 40% in articles with no COI (p=0.017, CMH, adjusted for study result). In a stepwise logistic regression with journal, funding, study type, study result, and COI only COI remained significant (OR=2.4, 95%CI 1.2–5.0, P=0.017). While all articles with a negative conclusion had a negative primary EP, 21 articles with clearly positive conclusions had a negative primary EP. The most common reasons for such finding were: positive secondary EP (6 studies), experimental agent had better toxicity profile (5), non-statistically significant difference in favour of experimental agent (4). Conclusion: COI is associated with highly positive conclusions that use superlatives to promote the experimental arm No significant financial relationships to disclose. [Table: see text]

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)0.543
metaresearch head score (Gemma)0.802
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.563

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5430.802
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0190.020
Science and technology studies0.0010.006
Scholarly communication0.0090.005
Open science0.0030.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.742
GPT teacher head0.688
Teacher spread0.054 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainEvaluation
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

Citations6
Published2007
Admission routes1
Has abstractyes

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