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Can Single Use Negative Pressure Wound Therapy be an alternative method to manage keloid scarring? A preliminary report of a clinical and ultrasound/colour‐power‐doppler study

2012· article· en· W2004537585 on OpenAlexaboutno aff
Marco Fraccalvieri, A Sarno, Stefano Gasperini, E. Zingarelli, Raffaella Fava, Marco Salomone, Stefano Bruschi

Bibliographic record

VenueInternational Wound Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKeloidItchingVisual analogue scaleSurgeryPower dopplerWound healingUltrasonography

Abstract

fetched live from OpenAlex

Keloid scarring represents a pathological healing where primary healing phenomenon is deviated from normal. Pico is a single use negative pressure wound therapy system originally introduced to manage open or just closed wounds. Pico dressing is made of silicone, and distributes an 80 mmHg negative pressure across wound bed. Combination of silicon layer and continuous compression could be a valid method to manage keloid scarring. Since November 2011, three patients were enrolled and evaluated before negative pressure treatment, at end of treatment (1 month) and 2 months later, through Vancouver Scar Scale (VSS), Visual Analog Scale (VAS) and a scoring system for itching. Ultrasound (US) and colour-power-doppler (CPD) examination was performed to evaluate thickness and vascularisation of the scar. One patient was discharged from study after 1 week. In last two patients, VSS, VAS and itching significantly improved after 1 month therapy and the results were stable after 2 months without any therapy. At end of therapy, the 'appearance of palisade vessels' disappeared in both cases at CPD exam; US showed a thickness reduction (average 43·8%). We propose a well-tolerated, non invasive treatment to manage keloid scarring. Prospective studies are necessary to investigate whether these preliminary observations are confirmed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.131
GPT teacher head0.464
Teacher spread0.333 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes1
Has abstractyes

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