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Painless ulceration due to digital hypoperfusion ischaemic syndrome: case report and literature review

2023· review· en· W4383217565 on OpenAlexaff
Merna Adly, Malika A. Ladha, Régine Mydlarski, Paul Petrasek, Laurie Parsons

Bibliographic record

VenueJournal of Wound Care · 2023
Typereview
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSurgeryComplicationGangreneArteriovenous fistula

Abstract

fetched live from OpenAlex

Digital hypoperfusion ischaemic syndrome (DHIS), also known as steal syndrome, is a well recognised serious complication of haemodialysis (HD) access creation. The clinical presentation varies from cyanosis to tissue loss due to necrosis or gangrene. In this article, we present a case of painless digital ulceration due to DHIS and provide a review of the literature. A 40-year-old-female presented with multiple painless digital ulcerations of the left hand. Her medical profile included atherosclerotic disease, hypertension, hyperparathyroidism and type I diabetes causing retinopathy, peripheral neuropathy, gastroparesis and end-stage renal disease (ESRD). Her ESRD required HD with the construction of a left-arm basilic vein transposition arteriovenous fistula (AVF). A year later, she developed intermittent, painless ulcerations of the left hand. A Doppler ultrasound confirmed the diagnosis of DHIS. The patient was treated with AVF ligation surgery. At six months postoperatively, she had near complete re-epithelialisation of her ulcers. This case is unique in that the patient did not have preceding pain, likely due to her underlying diabetic neuropathy. While DHIS in haemodialysis patients with AVF is well documented in literature, digital ulceration in this context is an advanced form of this condition. Early recognition of digital ulceration as a complication of DHIS may enable early intervention and prevent permanent damage.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.056
GPT teacher head0.405
Teacher spread0.349 · 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.

Study designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

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