Glutamine and hyperammonemic crises in patients with urea cycle disorders
Bibliographic record
Abstract
UNLABELLED: Blood ammonia and glutamine levels are used as biomarkers of control in patients with urea cycle disorders (UCDs). This study was undertaken to evaluate glutamine variability and utility as a predictor of hyperammonemic crises (HACs) in UCD patients. METHODS: The relationships between glutamine and ammonia levels and the incidence and timing of HACs were evaluated in over 100 adult and pediatric UCD patients who participated in clinical trials of glycerol phenylbutyrate. RESULTS: The median (range) intra-subject 24-hour coefficient of variation for glutamine was 15% (8-29%) as compared with 56% (28%-154%) for ammonia, and the correlation coefficient between glutamine and concurrent ammonia levels varied from 0.17 to 0.29. Patients with baseline (fasting) glutamine values >900 μmol/L had higher baseline ammonia levels (mean [SD]: 39.6 [26.2]μmol/L) than patients with baseline glutamine ≤ 900 μmol/L (26.6 [18.0]μmol/L). Glutamine values >900 μmol/L during the study were associated with an approximately 2-fold higher HAC risk (odds ratio [OR]=1.98; p=0.173). However, glutamine lost predictive significance (OR=1.47; p=0.439) when concomitant ammonia was taken into account, whereas the predictive value of baseline ammonia ≥ 1.0 upper limit of normal (ULN) was highly statistically significant (OR=4.96; p=0.013). There was no significant effect of glutamine >900 μmol/L on time to first HAC crisis (hazard ratio [HR]=1.14; p=0.813), but there was a significant effect of baseline ammonia ≥ 1.0 ULN (HR=4.62; p=0.0011). CONCLUSIONS: The findings in this UCD population suggest that glutamine is a weaker predictor of HACs than ammonia and that the utility of the predictive value of glutamine will need to take into account concurrent ammonia levels.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".