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Record W3194308109 · doi:10.1093/inthealth/ihab050

International stem cell tourism: a critical literature review and evidence-based recommendations.

2022· article· en· W3194308109 on OpenAlexaff

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStem cellMedical tourismScopusTourismLegislatureStem-cell therapyMEDLINE

Abstract

fetched live from OpenAlex

Stem cell tourism is an emerging area of medical tourism activity. Frustrated by the slow translation of stem cell research into clinical practice, patients with debilitating conditions often seek therapeutic options that are not appropriately regulated. This review summarises recent developments in the field of stem cell tourism and provides clinicians with the information necessary to provide basic pretravel health advice to stem cell tourists. PubMed and Scopus databases were consulted for relevant publications, using combinations of the terms 'stem cell', 'tourism', 'regenerative medicine', 'international', 'travel medicine' and 'environmental health'. The leading countries in the international stem cell tourism market are the USA, China, India, Thailand and Mexico. As the majority of clinics offering stem cell therapies are based in low- and-middle-income countries, stem cell tourists place themselves at risk of receiving an unproven treatment, coupled with the risk of travel-related illnesses. These clinics do not generally provide even basic travel health information on their websites. In addition to often being ineffective, stem cell therapies are associated with complications such as infection, rejection and tumorigenesis. Physicians, researchers, regulatory bodies, advocacy groups and medical educators are encouraged to work together to improve patient and physician education and address current legislative deficiencies.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.313
Teacher spread0.260 · 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 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

Citations8
Published2022
Admission routes1
Has abstractyes

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