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Record W2010807192 · doi:10.1007/s10227-004-0106-8

Treatment of Chronic Nonhealing Leg Ulceration with Gaseous Nitric Oxide: A Case Study

2004· article· en· W2010807192 on OpenAlexaff
Christopher C.J. Miller, Minna Miller, Abdi Ghaffari, Brian Kunimoto

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2004
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicineDermatologyNitric oxideSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the best clinical practice, chronic nonhealing ulcers of the lower extremities present a significant challenge. Nitric oxide (NO) has been shown to play a significant role in biological functions including wound healing and as an antimicrobial agent in nonspecific immune response. OBJECTIVE: Our goal was to study the effect of gaseous NO (gNO) administered directly to a two-year-old nonhealing chronic venous ulcer in a 55-year-old male presenting with a 30-year history of severe venous disease. METHODS: gNO (200 ppm) was applied to the lower extremity using a delivery system connected to a "single patient use" plastic boot, at 1.0 L/min. RESULTS: The patient received an average of 8.1-h treatments for 14 consecutive nights. On day 0 the wound was malodorous and covered by bacterial biofilm with little healthy granulation tissue present. Following 3 days of gNO treatment, healthy granulation tissue was noted with absence of malodorous odor. At day 14, the ulcer was significantly reduced in size (p = 0.014) and almost completely reepithelialized. Day 10 post-treatment did not reveal any deterioration in healing. Six weeks later, the wound was 90% healed. At 26 weeks post gNO discontinuation, the ulcer was completely healed. CONCLUSIONS: This single case study demonstrated that gNO as a topical agent was well tolerated by the patient without any report of discomfort or side effect. The result of wound healing was very promising and warrants future exploration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.040
GPT teacher head0.316
Teacher spread0.275 · 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 designCase report
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

Citations47
Published2004
Admission routes1
Has abstractyes

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