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Record W226809072

The Food and Drug Administration, regenerative sciences, and the regulation of autologous stem cell therapies.

2011· article· en· W226809072 on OpenAlexaff
Barbara von Tigerstrom

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFood and drug administrationStem cellScope (computer science)Regenerative medicineBusinessRegulatory scienceLaw and economicsBiotechnologyRisk analysis (engineering)MedicineEconomicsBiologyCell biologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

In an ongoing dispute, FDA asserts that autologous cultured stem cells used in treatments for orthopedic conditions are drugs and biological products subject to licensing and good manufacturing practice requirements, while the company providing the treatments claims FDA has no authority over its activities. This article uses the dispute as a focal point to explore current issues relating to the regulation of innovative stem cell-based products, including the impact of regulation on access to new treatments, the role of other oversight mechanisms, the particular challenges of autologous stem cell products and the scope of existing flexibilities in the regulatory framework.

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)0.015
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0200.009
Insufficient payload (model declined to judge)0.0060.002

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 head0.040
GPT teacher head0.239
Teacher spread0.198 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2011
Admission routes1
Has abstractyes

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