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Record W2769984456 · doi:10.1159/000484630

Reporting of Randomized Trials in Common Cancers in the Lay Media

2017· article· en· W2769984456 on OpenAlexaff
Domen Ribnikar, Hadar Goldvaser, Alberto Ocaña, Arnoud J. Templeton, Boštjan Šeruga, Eitan Amir

Bibliographic record

VenueOncology · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Center for Theoretical SciencesInternational Business Machines Corporation
KeywordsMedicineRandomized controlled trialInternal medicineBreast cancerChemotherapyCancerOncology

Abstract

fetched live from OpenAlex

BACKGROUND: Limited data exist about the role of the lay media in the dissemination of results of randomized controlled trials (RCTs) in common cancers. METHODS: Completed phase III RCTs evaluating new drugs in common cancers between January 2005 and October 2016 were identified from ClinicalTrials.gov. Lay media reporting was identified by searching LexisNexis Academic. Scientific reporting was defined as presentation at an academic conference or publication in full. Associations between reporting in the lay media before scientific reporting and study design and sponsorship were evaluated using logistic regression. RESULTS: Of 180 RCTs identified, 52% were reported in the lay media and in 27%, lay media reporting occurred before scientific reporting with an increasing trend over time (p = 0.009). Reporting in the lay media before scientific reporting was associated with positive results (OR: 2.10, p = 0.04), targeted therapy compared to chemotherapy (OR: 4.75, p = 0.006), immunotherapy compared to chemotherapy (OR: 7.60, p = 0.02), and prostate cancer compared to breast cancer (OR: 3.25, p = 0.02). CONCLUSIONS: Over a quarter of all RCTs in common cancers are reported in the lay media before they are reported scientifically with an increasing proportion over time. Positive trials, studies in prostate cancer, and trials of immunotherapy are associated with early reporting in the lay media.

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)0.214
metaresearch head score (Gemma)0.772
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2140.772
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 integrity0.0000.001
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 head0.813
GPT teacher head0.719
Teacher spread0.094 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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