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Granulation Tissue of Chronic Pressure Ulcers as a Predictive Indicator of Wound Closure

2011· article· en· W2017816996 on OpenAlexaboutno aff
Jennifer T. Wyffels, Laura E. Edsberg

Bibliographic record

VenueAdvances in Skin & Wound Care · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGranulation tissueMedicineGranulationAdobe photoshopWound closureChronic woundDigital image analysisWound healingSurgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: : To describe the temporal relationship between the quantity of granulation tissue in a chronic pressure ulcer (PrU) and its clinical outcome. DESIGN: : Study participants were seen on days 0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 14, 21, 28, 35, and 42. On each visit, the wounds were digitally photographed with a 3-cm calibration target. Images were analyzed using VeV MD (version 1.1.14; VERG Inc, Winnipeg, Manitoba, Canada) and Adobe Photoshop CS3 Extended (version 10.0.1; Adobe Systems Inc, San Jose, California). Granulation tissue was selected from calibrated digital images by 1 of 2 methods: manual selection and automated selection. Granulation tissue area was expressed as a percentage of total wound area. SETTING: : Academic research laboratory. PARTICIPANTS: : Thirty-one chronic PrUs were observed in 27 subjects. MAIN OUTCOME MEASURES: : Quantitative measure of granulation tissue area. MAIN RESULTS: : There was no relationship between the amount of granulation tissue expressed as a percentage of the total PrU area and wound outcome. CONCLUSIONS: : This study is the first to both quantitatively measure the amount of granulation tissue in a chronic PrU and attempt to correlate it to wound outcome. Although counterintuitive, the amount of granulation tissue was not predictive of outcome, and no temporal trends could be described.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score1.000

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)0.0010.000

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 head0.365
Teacher spread0.347 · 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.

Study designObservational
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

Citations18
Published2011
Admission routes1
Has abstractyes

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