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Record W2732157852 · doi:10.1126/scitranslmed.aag0426

Marketing of unproven stem cell–based interventions: A call to action

2017· review· en· W2732157852 on OpenAlexaff
Douglas Sipp, Timothy Caulfield, Jane Kaye, Jan Barfoot, Clare Blackburn, Sarah Chan, Michele De Luca, Alastair Kent, Christopher McCabe, Megan Munsie, Margaret Sleeboom‐Faulkner, Jeremy Sugarman, Esther van Zimmeren, Amy Zarzeczny

Bibliographic record

VenueScience Translational Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of ReginaUniversity of Alberta
FundersMedical Research CouncilFondation Brocher
KeywordsStem cellPromotion (chess)Action (physics)Call to actionPsychological interventionBusinessMedicineMarketingPolitical scienceBiologyCell biology

Abstract

fetched live from OpenAlex

Commercial promotion of unsupported therapeutic uses of stem cells is a global problem that has proven resistant to regulatory efforts. Here, we suggest a coordinated approach at the national and international levels focused on engagement, harmonization, and enforcement to reduce the risks associated with direct-to-consumer marketing of unproven stem cell treatments.

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)0.011
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.380
GPT teacher head0.517
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations198
Published2017
Admission routes1
Has abstractyes

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