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Record W4401101336 · doi:10.1111/iwj.14949

The muscle pump activator device: From evidence to lived experiences

2024· review· en· W4401101336 on OpenAlexaff
R. Gary Sibbald, Ryan S.Q. Geng, Jacqueline Slomovic, Michael Stacey

Bibliographic record

VenueInternational Wound Journal · 2024
Typereview
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsHamilton Health SciencesPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychosocialChronic woundEdemaWound healingDiabetic footSurgeryWound careQuality of life (healthcare)Intensive care medicinePhysical therapyDiabetes mellitus

Abstract

fetched live from OpenAlex

A chronic wound is one that fails to progress through a normal timely sequence of repair, or in which the repair process fails to restore anatomic and functional integrity after 3 months. The most common chronic wounds include venous, ischaemic and mixed leg ulcers, diabetic foot ulcers and pressure injuries. Chronic wounds place immense physical and psychosocial burden on patients and exact heavy costs for healthcare systems, with many patients continuing to live with chronic wounds even after all management options have been exhausted. The muscle pump activator (MPA) device can be used to bridge this therapeutic gap. By stimulating the common peroneal nerve to activate venous muscle pump of the leg and foot, the MPA device increases blood flow to the lower leg and foot to improve conditions for healing. Currently, evidence in the literature exist to show that the MPA device improves wound outcomes over standard compression therapy, decreases edema and increases wound healing rates. In this review, we also present a series of chronic wound patients treated with the MPA device in multicentre clinics to demonstrate the ability of the MPA device to improve wound outcomes, reduce pain and edema and improve patient quality of life.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.128
GPT teacher head0.437
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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