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Record W7144141982 · doi:10.71465/ajbe1552

Biomedical Engineering in the Treatment of Wound Healing: Innovations and Applications

2023· article· W7144141982 on OpenAlexaff
Dr. Clara Hughes, Dr. Lucas Carter

Bibliographic record

VenueAmerican Journal of Biomedical Engineering · 2023
Typearticle
Language
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWound healingTissue engineeringWound careRegenerative medicineProcess (computing)Wound dressing

Abstract

fetched live from OpenAlex

Wound healing is a complex and dynamic process that involves tissue repair and regeneration, essential for restoring normal function after injury. Biomedical engineering has made significant contributions to enhancing wound healing treatments by developing advanced technologies such as tissue-engineered skin substitutes, growth factor delivery systems, and smart wound dressings. This article explores the role of biomedical engineering in improving wound healing, focusing on innovations in wound care products, regenerative therapies, and the use of biomaterials for enhanced tissue repair. The article also discusses the challenges and future directions in wound healing treatments, particularly in chronic wounds and diabetic ulcers.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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 designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

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