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Record W2727374895 · doi:10.1111/iwj.12766

Refractory venous leg ulcers: observational evaluation of innovative new technology

2017· article· en· W2727374895 on OpenAlexaffabout
Connie Harris, Amanda Loney, Jillian Brooke, Amanda Charlebois, Lucy Coppola, Sowmil Mehta, Norman Flett

Bibliographic record

VenueInternational Wound Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsMcMaster UniversityWindsor Regional HospitalWestern UniversitySt. Joseph’s Healthcare HamiltonHome and Community Care Support Services
Fundersnot available
KeywordsMedicineFormularyObservational studyRefractory (planetary science)SurgeryWound careWound healingInternal medicine

Abstract

fetched live from OpenAlex

This observational case series reports the evaluation of a novel neuromuscular electrical stimulation device (geko™) that stimulates the common peroneal nerve at the fibular head as an adjunctive therapy in patients with non-healing venous leg ulcers. The aim was to evaluate and determine if the geko™ device was effective in this population and should be added to the medical supply formulary. Patients whose wounds had failed to heal within 24 weeks of standard therapy were identified in two community settings in Ontario. A total of 11 patients consented to the evaluation with a combined 107-year history of recalcitrant leg ulcers. Although the pre-geko™ healing rate was unknown, all ulcers were considered non-healing. With geko™, the average weekly percentage reduction in surface area for all patients was 4·5% and for the six adherent to geko™ and best practices 7·0%. By comparison, the average weekly percentage reduction for measurable wounds in the five non-adherent patients was 1·8%. Requirements for success appear to include an arterial status adequate for healing, effective and prompt management of wound infections and adherence to the treatment schedule. The geko™ device has been added to the medical supply formulary in one centre and is pending in the other.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.144
GPT teacher head0.421
Teacher spread0.277 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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