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Record W2169436882 · doi:10.1586/erd.10.40

Novel wound sealants: biomaterials and applications

2010· article· en· W2169436882 on OpenAlex

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

VenueExpert Review of Medical Devices · 2010
Typearticle
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsDefence Research and Development Canada
FundersDefence Research and Development Canada
KeywordsSealantBiocompatible materialWound healingFibrin Tissue AdhesiveFibrinWound dressingSurgical woundSelf-healing hydrogelsMedicineDentistrySurgeryBiomedical engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Wound sealants provide an excellent alternative for closing surgical and non-surgical wounds, as well as stopping external bleeding for prehospital trauma injuries. Numerous biomaterials have been investigated to address specific requirements for their use as suitable wound sealants. This article focuses on the development of new wound sealant biomaterials and recent advances in the surgical applications of wound sealants. In the past 5 years, many new sealant materials had been reported, including keratin, mussel-adhesive proteins, dendrimers and in situ-forming hydrogels. Fibrin sealants remain the most clinically studied for a variety of surgical procedures, while clinical experience with wound sealants for orthopedic surgery is limited. Both liquid and solid wound sealants have been developed and found effective by possessing strong adhesive properties. Biocompatible and biodegradable wound sealants hold much promise in eventually replacing sutures in most surgical procedures.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.024
GPT teacher head0.377
Teacher spread0.352 · 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