Severity-adjusted evaluation of liver transplantation on health outcomes in urea cycle disorders
Bibliographic record
Abstract
PURPOSE: Liver transplantation (LTx) is performed in individuals with urea cycle disorders when medical management (MM) insufficiently prevents the occurrence of hyperammonemic events. However, there is a paucity of systematic analyses on the effects of LTx on health-related outcome parameters compared to individuals with comparable severity who are medically managed. METHODS: We investigated the effects of LTx and MM on validated health-related outcome parameters, including the metabolic disease course, linear growth, and neurocognitive outcomes. Individuals were stratified into "severe" and "attenuated" categories based on the genotype-specific and validated in vitro enzyme activity. RESULTS: LTx enabled metabolic stability by prevention of further hyperammonemic events after transplantation and was associated with a more favorable growth outcome compared with individuals remaining under MM. However, neurocognitive outcome in individuals with LTx did not differ from the medically managed counterparts as reflected by the frequency of motor abnormality and cognitive standard deviation score at last observation. CONCLUSION: Whereas LTx enabled metabolic stability without further need of protein restriction or nitrogen-scavenging therapy and was associated with a more favorable growth outcome, LTx-as currently performed-was not associated with improved neurocognitive outcomes compared with long-term MM in the investigated urea cycle disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".