Individualized long-term outcomes in blood phenylalanine concentrations and dietary phenylalanine tolerance in 11 patients with primary phenylalanine hydroxylase (PAH) deficiency treated with Sapropterin-dihydrochloride
Bibliographic record
Abstract
We analyzed long-term sustainability of improved blood Phenylalanine (Phe) control and changes to dietary Phe tolerance in 11 patients (1 month to 16 years), with various forms of primary PAH deficiency (classic, moderate, severe phenylketonuria [PKU], mild hyperphenylalaninemia [HPA]), who were treated with 15-20mg/kg/d Sapropterin-dihydrochloride during a period of 13-44 months. 7/11 patients had a sustainable, significant reduction of baseline blood Phe concentrations and 6 of them also had an increase in mg/kg/day Phe tolerance. In 2 patients with mild HPA, blood Phe concentrations remained in the physiologic range even after a 22 and 36% increase in mg/kg/day Phe tolerance and an achieved Phe intake at 105% and 268% of the dietary reference intake (DRI) for protein. 2 of these responders had classic PKU. 1 patient with mild HPA who started treatment at 2 months of life, had a significant and sustainable reduction in pretreatment blood Phe concentrations, but no increase in the mg/kg/day Phe tolerance. An increase in Phe tolerance could only be demonstrated when expressing the patient's daily Phe tolerance with the DRI for protein showing an increase from 58% at baseline to 78% of normal DRI at the end of the observation. Long-term follow-up of patients with an initial response to treatment with Sapropterin is essential to determine clinically meaningful outcomes. Phenylalanine tolerance should be expressed in mg/kg/day and/or % of normal DRI to differentiate medical therapy related from physiologic growth related increase in daily Phe intake.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".