Beneficial effects of L-ornithine L-aspartate for prevention of overt hepatic encephalopathy in patients with cirrhosis: a systematic review with meta-analysis
Bibliographic record
Abstract
The present systematic review with meta-analysis was undertaken to review the evidence base in support of a beneficial effect of L-ornithine L-aspartate (LOLA) for the prevention/prophylaxis of overt hepatic encephalopathy (OHE) in patients with cirrhosis. Using appropriate keywords and electronic and manual searches together with established inclusion/exclusion criteria, six randomized controlled trials (RCTs) for a total of 384 patients were identified five of which were of high quality and low risk of bias according to Jadad-Cochrane criteria. Treatment with LOLA resulted in significant reductions in the risk of progression to OHE in MHE patients (3 studies) with RR: 0.23 [95% CI: 0.07, 0.73], p < 0.01. LOLA was also effective for secondary OHE prophylaxis with RR: 0.389 [95% CI: 0.174-0.870] p < 0.002 as well as for primary prophylaxis for OHE following acute variceal bleeding [RR: 0.42 [95% CI: 0.16-0.98] p < 0.03 and for OHE prophylaxis post-TIPSS [RR: 0.30 [95% CI: 0.03-2.66] compared to placebo/no intervention in all cases. OHE prevention/prophylaxis was accompanied by significant reductions of blood ammonia. Both oral and intravenous formulations of LOLA appeared to be effective for the prevention of progression to OHE in patients with MHE. These findings provide the first direct evidence of potential benefit of LOLA for the prevention of OHE in cirrhosis across a range of clinical presentations.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".