Painless ulceration due to digital hypoperfusion ischaemic syndrome: case report and literature review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".