Bile acids and coagulation factors
Bibliographic record
Abstract
OBJECTIVE: To examine whether there is an association between bile acids and coagulation factors in children with chronic liver disease. METHODS: Forty-five patients (age 2.8 months-18.8 years) were included in this cross-sectional observational study carried out at a single tertiary referral center. Coagulation factors, prothrombin time, albumin, and total fasting serum bile acids were analyzed. The international normalized ratio (INR) and the pediatric end-stage liver disease score were calculated. RESULTS: The 12 patients with bile acids more than 200 μmol/l showed a significant positive correlation between bile acids and factor V (FV), FVII, and prothrombin time (r(s) = 0.80, 0.72, 0.60, P<0.05) and a significant negative correlation between bile acids and INR (r(s) = -0.58, P<0.05). Conversely, in the group with bile acids less than 200 μmol/l, there was a significant negative correlation between bile acids and FVII and FIX (r(s) = -0.41 and -0.41, P<0.05) and a positive, albeit nonsignificant, correlation between bile acids and INR. No in-vitro analytical interference between bile acids and coagulation factors was found. Patients with bile acids more than 200 μmol/l had a significantly worse outcome than patients with lower levels of bile acids. CONCLUSION: A positive correlation was found between bile acids and coagulation factors in patients with bile acids more than 200 μmol/l. Coagulation factors may be questionable as prognostic markers in patients with markedly elevated bile acids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".