Multiple biopsy passes and the risk of complications of percutaneous liver biopsy
Bibliographic record
Abstract
BACKGROUND AND AIM: To minimize the sample variability of liver biopsy, the tissue length should be at least 25 mm. Consequently, more than one biopsy pass is needed with cutting biopsy needles. We aimed to investigate the risk factors of biopsy-related complication, including the number of biopsy passes. METHODS: All consecutive liver biopsies performed between 2005 and 2014 were included. Biopsies were ultrasound assisted and performed with cutting biopsy needles. A complication was an event where the patient visited a healthcare provider because of biopsy-related complaints. Complications followed by hospitalization 2 or more days or intervention were considered severe. RESULTS: In total, 1806 liver biopsies were analyzed. Overall, 102 (5.6%) complications were observed, of which 31 (1.7%) were severe. One (0.06%) patient died. Common complications were pain (n=75/102; 74%) and bleeding (n=34/102; 33%). Two biopsy passes were not associated with an increased risk of complications compared with one biopsy pass [odds ratio (OR): 1.59; 95% confidence interval (CI): 0.83-3.04; P=0.16], whereas three or more biopsy passes increased this risk compared with one (OR: 2.97; 95% CI: 1.38-6.42; P=0.005) or two biopsy passes (OR: 1.87; 95% CI: 1.10-3.19; P=0.021). The risk of severe complications was not influenced by the number of biopsy passes (P>0.24). Hepatic malignancy (OR: 3.21; 95% CI: 1.18-8.73; P=0.022) and international normalized ratio 1.4 or more (OR: 7.03; 95% CI: 2.74-18.08; P<0.001) were risk factors of severe complications. CONCLUSION: Severe complication rate and mortality were low. Performing multiple biopsy passes was not associated with severe complications, whereas hepatic malignancy or elevated international normalized ratio were associated with an increased risk.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".