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Record W4402782826 · doi:10.1097/won.0000000000001112

Management of Pain in People Living With Chronic Limb Threatening Ischemia

2024· review· en· W4402782826 on OpenAlexaff
Kevin Woo, Christine Murphy, Emily Gregg, Joshua Moralejo, Kimberly LeBlanc, Tim Brandys

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2024
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsContinental (Canada)Ottawa HospitalQueen's UniversityUniversity Health Network
Fundersnot available
KeywordsMedicineLimitingNarrative reviewQuality of life (healthcare)DiseaseIschemiaIntensive care medicineCritical limb ischemiaHealth carePhysical therapyArterial diseaseVascular diseaseSurgeryPathologyInternal medicineNursing

Abstract

fetched live from OpenAlex

Peripheral artery disease is a complex health condition. It is associated with atherosclerotic occlusive lesions in the arteries limiting normal blood flow, mostly involving the lower extremities, leading to chronic limb-threatening ischemia (CLTI). Chronic unrelenting ischemic leg pain can be debilitating and distressing, contributing to poor health-related quality of life. Comprehensive management of pain associated with CLTI requires multimodal approaches that draw on a range of strategies and specialist treatments delivered by an interdisciplinary team across various health care settings. We recognized a significant gap in evidence-based strategies that are accessible, appropriate, acceptable, effective, and safe for the elderly with CLTI-associated pain. We therefore conducted an umbrella review or overview of multiple existing reviews that employ a rigorous and transparent method to comprehensively identify and synthesize relevant literature including systematic, scoping, and narrative reviews. The purpose of this umbrella review was to aggregate and compare various management options to inform best practices and quality indicators for the management of ischemic pain in older patients with peripheral artery disease.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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
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

Citations3
Published2024
Admission routes1
Has abstractyes

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