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Record W4400886750 · doi:10.1097/asw.0000000000000146

Using Near-Infrared Spectroscopy and Education to Support Older Adults with Diabetic Foot Ulcers to Age-in-Place: A Case Series

2024· article· en· W4400886750 on OpenAlexaff
Tracey Rickards, C J Roberts, Surajudeen Shittu, Chris Boodoo, Karen Cross

Bibliographic record

VenueAdvances in Skin & Wound Care · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsNova Scotia Health AuthorityUniversity of New Brunswick
Fundersnot available
KeywordsMedicineFoot (prosody)GerontologyStressorDiabetic footMultidisciplinary approachSocial supportTelemedicineHealth careDiabetes mellitusPsychologyPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT: The objective of this article is to demonstrate the added value of foot care provided by an RN with foot care training to older adults in their home by focusing on four older adults with diabetic foot ulcers. The RN used a mobile multispectral near-infrared spectroscopy device to enhance the assessment of the diabetic foot ulcers. The Mobile Seniors Wellness Network methodically engaged with English-speaking adults 55 years and older living within a 90-minute radius of the city's community health center. Older adults were referred to the research project through various sources. The participation group included 366 participants with varying holistic healthcare concerns and financial stressors that impacted their ability to age well in place. Over the course of visits by the RN and registered social worker, positive outcomes were facilitated through the collaboration of the participant and the multidisciplinary team, thus enhancing the individual's confidence to remain at home longer. In a time of ongoing provincial health crisis, it may be cost-effective to provide in-home support to those who want to age well in their communities by deploying a Mobile Seniors Wellness Network system throughout the province and enhancing the RN's assessment of feet with a portable and innovative technology tool.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.315
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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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