Defining tetrahydrobiopterin responsiveness in phenylketonuria: Survey results from 38 countries
Bibliographic record
Abstract
A subset of patients with phenylketonuria benefit from treatment with tetrahydrobiopterin (BH4), although there is no consensus on the definition of BH4 responsiveness. The aim of this study therefore was to gain insight into the definitions of long-term BH4 responsiveness being used around the world. We performed a web-based survey targeting healthcare professionals involved in the treatment of PKU patients. Data were analysed according to geographical region (Europe, USA/Canada, other). We analysed 166 responses. Long-term BH4 responsiveness was commonly defined using natural protein tolerance (95.6%), improvement of metabolic control (73.5%) and increase in quality of life (48.2%). When a specific value for a reduction in phenylalanine concentrations was reported (n = 89), 30% and 20% were most frequently used as cut-off values (76% and 19% of respondents, respectively). When a specific relative increase in natural protein tolerance was used to define long-term BH4 responsiveness (n = 71), respondents most commonly reported cut-off values of 30% and 100% (28% of respondents in both cases). Respondents from USA/Canada (n = 50) generally used less strict cut-off values compared to Europe (n = 96). Furthermore, respondents working within the same center answered differently. The results of this study suggest a very heterogeneous situation on the topic of defining long-term BH4 responsiveness, not only at a worldwide level but also within centers. Developing a strong evidence- and consensus-based definition would improve the quality of BH4 treatment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".