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Record W2329642009 · doi:10.15436/2381-0858.16.011

Application of Adipose Derived Stem Cells for Treatment of Chronic Wounds

2016· article· en· W2329642009 on OpenAlexaff
Reza B. Jalili

Bibliographic record

VenueInvestigative Dermatology and Venereology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of British ColumbiaInternational Collaboration On Repair Discoveries
Fundersnot available
KeywordsAdipose tissueStem cellWound healingMedicineChronic woundSurgeryBioinformaticsIntensive care medicineBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Chronic wounds, such as pressure, venous, or diabetic ulcers, fail to progress through the orderly wound healing process. Due to limitations in current treatments, stem cell therapy has been considered as an alternative therapeutic approach to chronic wounds throughout the past decade. The use of adipose derived stem cells (ASCs) has been both a promising and practical treatment because of their relatively non-invasive extraction, high proliferation rate, and ability to differentiate into several mesodermal lineages. This mini-review provides a concise overview on the role of ASCs in wound healing and studies that have examined the application of ASCs in the treatment of chronic wounds.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.115
GPT teacher head0.389
Teacher spread0.274 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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