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Record W2967819101 · doi:10.1002/adhm.201900764

Bioinorganics and Wound Healing

2019· review· en· W2967819101 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

VenueAdvanced Healthcare Materials · 2019
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWound healingWound careBiomedical engineeringIntensive care medicineMedicineSurgery

Abstract

fetched live from OpenAlex

Wound dressings and the healing enhancement (increasing healing speed and quality) are two components of wound care that lead to a proper healing. Wound care today consists mostly of providing an optimal environment by removing waste and necrotic tissues from a wound, preventing infections, and keeping the wounds adequately moist. This is however often not enough to re-establish the healing process in chronic wounds; with the local disruption of vascularization, the local environment is lacking oxygen, nutrients, and has a modified ionic and molecular concentration which limits the healing process. This disruption may affect cellular ionic pumps, energy production, chemotaxis, etc., and will affect the healing process. Biomaterials for wound healing range from simple absorbents to sophisticated bioactive delivery vehicles. Often placing a material in or on a wound can change multiple parameters such as pH, ionic concentration, and osmolarity, and it can be challenging to pinpoint key mechanism of action. This article reviews the literature of several inorganic ions and molecules and their potential effects on the different wound healing phases and their use in new wound dressings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.104
GPT teacher head0.428
Teacher spread0.323 · 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