Is there an association between arteriovenous fistulas and axillary artery aneurysms? A report of two cases
Bibliographic record
Abstract
INTRODUCTION: Axillary artery aneurysms are rare vascular conditions that can present with various clinical manifestations, including neurological deficits and vascular compromise. While the underlying pathophysiology remains complex and multifactorial, potential associations with trauma, arteriovenous fistula formation, and atherosclerosis have been reported. PRESENTATION OF CASE: Two male patients, aged 33 and 38, with a history of kidney transplantation and previous arteriovenous fistula (AVF) presented with symptoms of upper limb ischemia and neurological compromise. Imaging revealed large axillary artery aneurysms. Open surgical repair was performed for both cases. Two weeks after discharge, one patient showed good pronation and supination with mildly limited extension. The other patient's wrist drop gradually improved with physiotherapy. DISCUSSION: Multifactorial pathophysiology encompassed altered blood flow dynamics, inflammation, and the underlying vascular pathology. Chief complaints and prior vascular interventions contributed. Open surgical repair was preferred to endovascular approaches, achieving favorable outcomes. CONCLUSION: Axillary artery aneurysms in patients with a history of AVF are rare but potentially serious complications. The cases highlight the complexity of axillary artery aneurysms and the need for careful evaluation and surgical intervention This strategy is crucial to prevent potential complications and optimize patient outcomes. Further research is needed to elucidate the precise pathophysiology and the potential association between AVF and the subsequent development of axillary artery aneurysms. Increasing awareness among surgeons could enable earlier detection of aneurysms, thereby reducing the risk of complications.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".