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Record W3095023472 · doi:10.3389/frym.2020.539007

Closing Wounds With Light?

2020· article· en· W3095023472 on OpenAlex
Irene Guzmán-Soto, Christopher D. McTiernan, Emilio I. Alarcón

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers for Young Minds · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaMinistero dello Sviluppo EconomicoCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsClosing (real estate)MedicineEpithelial tissueSurgeryProcess (computing)Scar tissueDermatologyComputer sciencePathologyBusinessEpithelium

Abstract

fetched live from OpenAlex

Small skin wounds in healthy people heal and close themselves, however healing of larger and stubborn wounds may need some form of medical treatment. Typically, stitches are used to close wounds and hold various tissues together. While the techniques and materials involved in closing wounds have improved over the years, the one problem that still remains with their use is scarring. To prevent scarring, a variety of glue-like materials, called tissue adhesives, have been created to hold opposing tissues together and fill larger tissue gaps. While tissue glues are used to close some wounds, they harden quickly and are not very strong, which prevents their use in applications where the appearance of the healed wound is important. To gain more control over the tissue bonding process, light-mediated techniques called photobonding have been developed.

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.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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