Recommendations for the use of sapropterin in phenylketonuria
Bibliographic record
Abstract
Phenylketonuria (PKU) is an inherited disorder of phenylalanine (Phe) metabolism. Until recently, the only treatment for PKU was a Phe-restricted diet. Increasing evidence of suboptimal outcomes in diet-treated individuals, inconsistent PKU management practices, and the recent availability of tetrahydrobiopterin (BH(4)) therapy have fueled the need for new management and treatment recommendations for this metabolic disorder. BH(4), now available as sapropterin dihydrochloride (sapropterin), may offer the potential for improved metabolic control as well as enhanced dietary Phe tolerance in some PKU patients. A group of metabolic dietitians from North America convened in June 2011 to draft recommendations for the use of sapropterin therapy in PKU. Physicians with extensive experience in PKU management were invited at a later date to contribute to the development of these recommendations. Based on extensive clinical experience and current evidence, the present recommendations provide guidance from patient selection and determination of sapropterin response to the long-term management of patients on sapropterin therapy. Target Phe levels, nutritional adequacy, neurocognitive screening and adherence to treatment are addressed to optimize patient outcomes.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".