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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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