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Record W2047792802 · doi:10.1002/term.20

A proposed definition of regenerative medicine

2007· article· en· W2047792802 on OpenAlexafffund
Abdallah S. Daar, Heather Greenwood

Bibliographic record

VenueJournal of Tissue Engineering and Regenerative Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity Health NetworkCentre for Global Health ResearchUniversity of Toronto
FundersU.S. National Library of MedicineCanadian Institutes of Health Research
KeywordsRegenerative medicineVariety (cybernetics)Field (mathematics)Engineering ethicsFunction (biology)Management scienceSubject (documents)Work (physics)Computer scienceData scienceEngineeringBiologyArtificial intelligenceMathematicsWorld Wide WebStem cell

Abstract

fetched live from OpenAlex

There exists a lack of consensus regarding a clear and precise definition of regenerative medicine. We suggest here a definition developed by the authors with input from researchers in the various contributing disciplines. This definition emphasizes the interdisciplinarity of the field, its goal of restoring impaired function, and the wide variety of technologies that can contribute to achieving this goal. By highlighting the lack of agreement regarding a definition of regenerative medicine, and by proposing our own definition, we hope to stimulate discussion on the subject within the field and to encourage the regenerative medicine community to work together to develop a consensus definition. We believe that a clear definition of regenerative medicine could help to unify the field and is essential to facilitate understanding among policy makers, funding agencies and the general public, as well as individuals from scientific and medical disciplines.

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)0.044
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0100.005
Science and technology studies0.0070.040
Scholarly communication0.0140.017
Open science0.0070.009
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0050.002

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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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 designTheoretical or conceptual
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

Citations165
Published2007
Admission routes2
Has abstractyes

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