Liver injury is associated with mortality in sickle cell disease
Bibliographic record
Abstract
BACKGROUND: Increased life expectancy in sickle cell disease (SCD) has resulted in greater recognition of the consequences of repeated intravascular vaso-occlusion and chronic haemolysis to multiple organ systems. AIM: To report the long-term consequences of liver dysfunction in SCD. METHODS: A cohort of SCD patients was prospectively evaluated at the National Institutes of Health (NIH) Clinical Center. The association of mortality with liver enzymes, parameters of liver synthetic function and iron overload was evaluated using Cox regression. RESULTS: Exactly, 247 SCD patients were followed up for 30 months of whom 22 (9%) died. After controlling for predictors, increased direct bilirubin (DB), ferritin, alkaline phosphatase and decreased albumin were independently associated with mortality. In a multivariable model, only high DB and ferritin remained significant. Ferritin correlated with hepatic iron content and total blood transfusions but not haemolysis markers. Forty patients underwent liver biopsies and 11 (28%) had fibrosis. Twelve of 26 patients (48%) had portal hypertension by hepatic venous pressure gradient (HVPG) measurements. All patients with advanced liver fibrosis had iron overload; however, most patients (69%) with iron overload were without significant hepatic fibrosis. Ferritin did not correlate with left ventricular dysfunction by echocardiography. DB correlated with bile acid levels suggesting liver pathology. Platelet count and soluble CD14 correlated with HVPG indicating portal hypertension. CONCLUSIONS: Ferritin and direct bilirubin are independently associated with mortality in sickle cell disease. Ferritin likely relates to transfusional iron overload, while direct bilirubin suggests impairment of hepatic function, possibly impairing patients' ability to tolerate systemic insults.
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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".