Long-term preservation of intellectual functioning in sapropterin-treated infants and young children with phenylketonuria: A seven-year analysis
Bibliographic record
Abstract
Sapropterin dihydrochloride has been approved for the treatment of hyperphenylalaninemia in infants and young children with phenylketonuria (PKU). Sapropterin can reduce phenylalanine (Phe) levels in tetrahydrobiopterin (BH4)-responsive patients, potentially preventing the intellectual impairment caused by elevated Phe levels. The long-term effect of sapropterin on intellectual functioning was assessed using the Full-Scale Intelligence Quotient (FSIQ) in 62 children who began treatment before the age of 6 years. Over each 2-year interval, the estimate of mean change in FSIQ was -0.5768 with a lower limit of the 95% confidence interval (CI) of -1.60. At the end of the follow-up period (Year 7), the least squares mean estimate of the change in FSIQ from baseline was 1.14 with a lower limit of the 95% CI of -3.53. These lower limits were both within the clinically expected variation of 5 points. During the whole study period, mean blood Phe levels remained within the American College of Medical Genetics (ACMG) target range of 120-360 μmol/L. In addition, height, weight, and head circumference were maintained within normal ranges throughout follow-up, as defined by growth charts from the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) for children below and above the age of 24 months, respectively. All patients (n = 65) enrolled in this study experienced at least one adverse event, as expected from previous studies. In conclusion, long-term use of sapropterin in individuals with PKU helps to control blood Phe, preserve intellectual functioning, and maintain normal growth in BH4-responsive children who initiated treatment between the ages of 0 to 6 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".