The first European guidelines on phenylketonuria: Usefulness and implications for BH<sub>4</sub> responsiveness testing
Bibliographic record
Abstract
Abstract Objective This study aimed to investigate and improve the usefulness of the 48‐hour BH4 loading test and to assess genotype for BH4 responsiveness prediction, using the new definition of BH4 responsiveness from the European guidelines, as well as an amended definition. Method Applying the definition of the European guidelines (≥100% increase in natural protein tolerance) and an amended definition (≥100% increase in natural protein tolerance or tolerating a safe natural protein intake) to a previous dataset, we first assessed the positive predictive value (PPV) of the 48‐hour BH4 loading test using a cutoff value of 30%. Then, we tried to improve this PPV by using different cutoff values and separate time points. Last, using the BIOPKU database, we compared predicted BH4 responsiveness (according to genotype) and genotypic phenotype values (GPVs) in BH4‐responsive and BH4‐unresponsive patients. Results The PPV of the 48‐hour loading test was 50.0% using the definition of the European guidelines, and 69.4% when applying the amended definition of BH4 responsiveness. Higher cutoff values led to a higher PPV, but resulted in an increase in false‐negative tests. Parameters for genotype overlapped between BH4‐responsive and BH4‐unresponsive patients, although BH4 responsiveness was not observed in patients with a GPV below 2.4. Conclusion The 48‐hour BH4 loading test is not as useful as previously considered and cannot be improved easily, whereas genotype seems mainly helpful in excluding BH4 responsiveness. Overall, the definition of BH4 responsiveness and BH4 responsiveness testing require further attention.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".