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Next-Generation Diagnostic Wound Dressings for Diabetic Wounds

2022· review· en· W4285089906 on OpenAlex
Tracy Fu, Polina Stupnitskaia, Simon Matoori

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.

Bibliographic record

VenueACS Measurement Science Au · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineDiabetic footAmputationDiabetes mellitusWound healingIntensive care medicineDiabetic foot ulcerComplicationSurgeryDiagnostic testChronic woundFoot (prosody)Debridement (dental)PathophysiologyPathologyPediatrics

Abstract

fetched live from OpenAlex

Chronic lower extremity wounds (diabetic foot ulcers) are a serious and prevalent complication of diabetes. These wounds exhibit low healing rates and present a high risk of amputation. Current diagnostic options for foot ulcers are limited to macroscopic wound analysis such as wound depth, implicated tissues, and infection. Molecular diagnostics promises to improve foot ulcer diagnosis, staging, and assessment of the treatment response. In this perspective, we report recent progress in understanding the pathophysiology of diabetic wound healing and point to recently emerged novel molecular targets for wound diagnostics. We discuss selected diagnostic wound dressings under preclinical development that detect one or several inflammatory markers, bacterial secretions, hyperglycemia, and mechanical stress. We also highlight key translational challenges of investigational diagnostic bandages for diabetic foot 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.

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)0.007
metaresearch head score (Gemma)0.003
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 score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.270
GPT teacher head0.379
Teacher spread0.109 · 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