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Record W4413674160 · doi:10.1111/hdi.70020

Progress in Diagnosis and Treatment of Limb Pain in Hemodialysis Patients With an Arteriovenous Fistula

2025· article· en· W4413674160 on OpenAlexvenueno aff
Cuiping Yuan, Lili Yin, Jiguang Song, Lina Ding, Yufei Yuan, Xianglei Kong

Bibliographic record

VenueHemodialysis International · 2025
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryArteriovenous fistulaHemodialysisCarpal tunnel syndromeComplex regional pain syndromeAxillary arteryDialysisStenosisRadiology

Abstract

fetched live from OpenAlex

In hemodialysis patients being dialyzed using an arteriovenous fistula, limb pain is a common problem with multifactorial etiologies, including puncture pain, dialysis access-associated ischemic steal syndrome, ischemic monomelic neuropathy, carpal tunnel syndrome, complex regional pain syndrome, and axillary artery dissection. The common causes of limb pain related to vascular access include direct puncture pain, vascular complications (such as stenosis, thrombosis, aneurysm), and nerve injury. The puncture pain related to dialysis access can be alleviated by local anesthetics (such as lidocaine gel), cryotherapy, and advanced catheter techniques (such as button hole method). The ischemic steal syndrome related to dialysis access requires surgical intervention. Common surgical methods include ligation or vascular reconstruction. Emergency ligation of the fistula is a common surgical approach for ischemic single nerve lesion. For carpal tunnel syndrome, surgical release or wearing a brace is needed to improve the condition. For complex regional pain syndrome, multimodal analgesia and sympathetic nerve block are required. The main treatment method for axillary artery dissection is vascular stent implantation. Early detection, early diagnosis, and early treatment are crucial for maintaining vascular access function and improving patient prognosis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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