Different treatment strategies for artery pseudoaneurysm: analysis of clinical characteristics on 14 cases
Bibliographic record
Abstract
Objective To summarize the clinical characteristics and results of different treatment strategies in patients with artery pseudoaneurysm after coronary angiography or percutaneous coronary intervention(PCI).Methods The clinical characteristics and different treatment strategies were summarized among patients who suffered artery pseudoaneurysm after coronary angiography or PCI from November 2004 to July 2008.Results We had 14 cases of arterial pseudoaneurysm.Two patients had diagnostic coronary angiography and 12 patients had percutaneous coronary intervention.Twelve patients had history of hypertension(92.9%) and 6 patients had diabetes(42.9%).One patient had radial artery pseudoaneurysm,2 cases with artery pseudoaneurysm were right brachial artery and 11 cases were femoral artery.One patient had bilateral femoral artery pseudoaneurysm.The average diameter of pseudoaneurysm cavity was 3.46±1.85cm(1.2~8.1cm).Eight cases had a large pseudoaneurysm(defined as diameter≥3cm).The average diameter of neck of artery pseudoaneurysm was(4.09±1.94)mm.One femoral artery pseudoaneurysm was spontaneously closed one day later.Nine of ten patients with pseudoaneurysms(69.2%) were successfully treated by ultrasound-guided manual compression.Three artery pseudoaneurysms were successfully closed by duplex-guided injection of Reptilase.One patient with right brachial pseudoaneurysm had surgery repair of pseudoaneurysm after failure of manual compression with duplex guiding and surgical repair needed local skin ulcer.Conclusion Most patients with arterial pseudoaneurysm could be successfully treated by ultrasound-guided manual compression.Patients who are failure by manual compression may need local injection of Reptilase by ultrasound guiding and manual compression.Direct surgery repair is occasionally needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".