Liver biopsy complication rates in patients with non-alcoholic fatty liver disease
Bibliographic record
Abstract
BACKGROUND: With new treatments for non-alcoholic fatty liver disease (NAFLD) on the horizon, it will be important to risk-stratify patients based on degree of fibrosis to allocate treatment to those at highest risk. No studies have examined the complication rates of liver biopsies in patients with NAFLD in the outpatient setting. METHODS: We conducted a retrospective chart review of all outpatient elective liver biopsies for NAFLD at a tertiary care centre over a 10-year period. Demographic variables and stage of fibrosis were recorded. Complications up to 1 week post-procedure were recorded. We used univariate logistic regression models to estimate the odds of major complications by fibrosis stage, age, sex, platelets, and international normalized ratio (INR). RESULTS: There were 582 biopsies reviewed in total. The mean age was 53 years. There was an even proportion of males to females. The mean fibrosis stage was 1.9; platelet count was 223.9, INR was 1, and partial thromboplastin time (PTT) was 31. Major complications occurred in 8 out of 582 biopsies (1.4%). Bleeding accounted for 6 of the major complications observed, while infection and pneumoperitoneum each occurred once. There were no statistically significant associations between age (odds ratio [OR] 0.97, 95% CI 0.92-1.03), female sex (OR 1.00, 95% CI 0.25-4.04), platelet count <150 (OR 0.59, 95% CI [-inf.], 3.86), INR >1.3 (OR 0.47, 95% CI 0.057-3.85), fibrosis stage, and complication rate. CONCLUSIONS: Our results are consistent with previous studies examining complication rates in other patient populations and clinical settings and support the overall safety of liver biopsies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".