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Record W2997559877 · doi:10.1177/1740774519895966

Trial marketing in the Pan-Canadian Early Detection of Lung Cancer Study

2020· article· en· W2997559877 on OpenAlexafffundabout
Janice Rudkowski, Gregory R. Pond, Alain Tremblay, Michael Johnston, Glen Goss, Garth Nicholas, Simon Martel, Geoffrey Liu, Heidi Schmidt, Martin C. Tammemägi, Sukhinder Atkar-Khattra, Ming‐Sound Tsao, Stephen Lam, John R. Goffin

Bibliographic record

VenueClinical Trials · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsJuravinski Cancer CentreBC Cancer AgencyMcMaster UniversityBrock UniversityUniversity of CalgaryPrincess Margaret Cancer CentreMemorial University of NewfoundlandUniversité LavalUniversity of OttawaUniversity Health NetworkToronto Metropolitan UniversityDalhousie University
FundersTerry Fox Research Institute
KeywordsContext (archaeology)Lung cancerMedicineObservational studyLung cancer screeningFamily medicineClinical trialInternal medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Recruitment to clinical trials is suboptimal, increasing costs, and delaying the potential implementation of clinical advances. Among other barriers, the lack of marketing experience among trialists may limit recruitment. In this observational study, in the context of the Pan-Canadian Early Detection of Lung Cancer Trial, we assessed the value of a motivational survey of study participants in planning a tailored advertising campaign and analysed the value of individual components of advertising in generating telephone calls to the study and recruited subjects. METHODS: The Pan-Canadian Early Detection of Lung Cancer Trial was a single arm study assessing risk modelling for lung cancer screening by low-dose computed tomography scan and autofluorescence bronchoscopy. Individuals were recruited to eight sites across Canada without a central marketing plan. On contact with the study, individuals reported how they heard about the study according to a predefined list. One site, the Juravinski Cancer Centre, worked with a marketing expert to develop a survey to assess participant motivations, source of study awareness, and personal habits. The survey was used to develop a media campaign for recruitment. Media events were collected from all sites. The primary analysis assessed the number of telephone contacts and recruited subjects associated with various media factors. Individual print media characteristics were assessed for their effect on recruitment. RESULTS: At all sites, 7059 individuals contacted the study, and 2537 were eligible and recruited. Among 52 individuals completing the Juravinski Cancer Centre survey, motivation included concern for personal risk of lung cancer (71%), followed by desire to contribute to a cure (67%), followed by personal knowledge of a person with lung cancer (50%). Most reported hearing of the study from the newspaper (58%) despite no print ad yet being distributed. With survey input, a newsprint campaign was executed. The number of media events varied by site (median: 13, range: 3-28). Among all recruits, 56.4% reported referral by newspaper followed by family/friend (14%). Telephone contacts and recruited subjects per event varied significantly by site, while unpaid media events appeared superior to paid events. Print media characteristics associated with increased telephone contacts and recruitment included use of a rational appeal (vs a mixed rational-emotional), less use of white space, and larger headline font. CONCLUSION: A survey of trial candidates provides useful information regarding personal motivation, media use, and lifestyle. Unpaid media events appear superior in generating recruitment, while print media may be superior to radio and television in selecting eligible recruits. The utility of individual print media characteristics appears to differ from the commercial advertising literature. Further research on marketing in clinical trials is encouraged to improve recruitment ( ClinicalTrials.gov registration: NCT00751660, https://clinicaltrials.gov/ct2/show/NCT00751660 ).

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)0.046
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 categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.881
GPT teacher head0.715
Teacher spread0.166 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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