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Record W2130995135 · doi:10.1377/hlthaff.2011.0329

Direct-To-Consumer Internet Promotion Of Robotic Prostatectomy Exhibits Varying Quality Of Information

2012· article· en· W2130995135 on OpenAlexaff
Joshua N. Mirkin, William T. Lowrance, Andrew Feifer, John P. Mulhall, James E. Eastham, Elena B. Elkin

Bibliographic record

VenueHealth Affairs · 2012
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsCredit Valley Hospital
FundersNational Cancer Institute
KeywordsPromotion (chess)The InternetGovernment (linguistics)Quality (philosophy)EnforcementProstatectomyBusinessMedicineMarketingPublic relationsPolitical scienceComputer scienceProstate cancerWorld Wide Web

Abstract

fetched live from OpenAlex

Robotic surgery to remove a cancerous prostate has become a popular treatment. Internet marketing of this surgery provides an intriguing case study of direct-to-consumer promotions of medical devices, which are more loosely regulated than pharmaceutical promotions. We investigated whether the claims made in online promotions of robotic prostatectomy were consistent with evidence from comparative effectiveness studies. After performing a search and cross-sectional analysis of websites that mentioned the procedure, we found that many sites claimed benefits that were unsupported by evidence and that 42 percent of the sites failed to mention risks. Most sites were published by hospitals and physicians, which the public may regard as more objective than pages published by manufacturers. Unbalanced information may inappropriately raise patients' expectations. Increasing enforcement and regulation of online promotions may be beyond the capabilities of federal authorities. Thus, the most feasible solution may be for the government and medical societies to promote the production of balanced educational material.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.420
GPT teacher head0.547
Teacher spread0.127 · 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 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

Citations50
Published2012
Admission routes1
Has abstractyes

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