Management of renal artery aneurysms: A retrospective study
Bibliographic record
Abstract
BackgroundAlthough renal artery aneurysms (RAAs) are rare and often asymptomatic with slow growth, their natural progression and optimal management are not well understood. Treatment recommendations for RAAs do exist; however, they are supported by limited data.MethodsA retrospective cohort study was conducted to explore the management of patients diagnosed with an RAA at our institution from January 1st, 2013, to December 31st, 2020. Patients were identified through a search of our radiological database, followed by a comprehensive chart review for further assessment. Data collection encompassed patient and aneurysm characteristics, the rationale for initial imaging, treatment, surveillance, and all-cause mortality.ResultsOne hundred eighty-five patients were diagnosed with or treated for RAAs at our center during this timeframe, with most aneurysms having been discovered incidentally. Average aneurysm size was 1.40 cm (±0.05). Of those treated, the mean size was 2.38 cm (±0.24). Among aneurysms larger than 3 cm in size, comprising 3.24% of the total cases, 83.3% underwent treatment procedures. Only 20% of women of childbearing age received treatment for their aneurysms. There was one instance of aneurysm rupture, with no associated mortality or significant morbidity.ConclusionsOur institution's management of RAAs over the period of the study generally aligned with guidelines. One potential area of improvement is more proactive intervention for women of childbearing age.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".