Neurodevelopmental Outcomes in Children With Inherited Liver Disease and Native Liver
Bibliographic record
Abstract
OBJECTIVE: To evaluate neurodevelopmental status among children with inherited cholestatic liver diseases with native liver and variables predictive of impairment. METHODS: Participants with Alagille syndrome (ALGS), progressive familial intrahepatic cholestasis (PFIC), and alpha 1 antitrypsin deficiency (A1AT) enrolled in a longitudinal, multicenter study and completed the Wechsler Preschool and Primary Scale of Intelligence-III or Intelligence Scale for Children-IV. Full Scale Intelligence Quotient (FSIQ) was analyzed continuously and categorically (>100, 85-99, 70-84, <70). Univariate linear regression was performed to study association between FSIQ and risk factors, stratified by disease. RESULTS: Two hundred and fifteen completed testing (ALGS n = 70, PFIC n = 43, A1AT n = 102); median age was 7.6 years (3.0-16.9). Mean FSIQ in ALGS was lower than A1AT (94 vs 101, P = 0.01). Frequency of FSIQ < 85 (>1 standard deviation [SD] below average) was highest in ALGS (29%) versus 18.6% in PFIC and 12.8% in A1AT, and was greater than expected in ALGS based on normal distribution (29% vs 15.9%, P = 0.003). ALGS scored significantly lower than test norms in almost all Wechsler composites; A1AT scored lower on Working Memory and Processing Speed; PFIC was not different from test norms. Total bilirubin, alkaline phosphatase, albumin, hemoglobin, and parental education were significantly associated with FSIQ. CONCLUSIONS: Patients with ALGS are at increased risk of lower FSIQ, whereas our data suggest A1AT and PFIC are not. A1AT and ALGS appear vulnerable to working memory and processing speed deficits suggestive of attention/executive function impairment. Malnutrition, liver disease severity, and sociodemographic factors appear related to FSIQ deficits, potentially identifying targets for early interventions.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".