Consensus Guideline for the Diagnosis and Treatment of Tyrosine Hydroxylase ( <scp>TH</scp> ) Deficiency
Bibliographic record
Abstract
Tyrosine hydroxylase (TH) catalyses the rate-limiting step in dopamine biosynthesis. Autosomal recessive tyrosine hydroxylase deficiency (THD) leads to clinical phenotypes reflecting the deficiency of dopamine, norepinephrine, or epinephrine in the central nervous system (CNS), presenting along a continuous spectrum from mild to severe forms of the disease. The diagnosis is suggested by the detection of low CSF homovanillic acid (HVA) and confirmed by identifying biallelic pathogenic variants in the TH gene. L-dopa/decarboxylase inhibitor (DCI) supplementation is often the first-line treatment, and most patients have a good therapeutic response. However, initiation of therapy can be challenging in patients with severe disease forms who develop L-dopa/DCI-induced dyskinesia. Therefore, alternative treatment options, such as monoamine oxidase (MAO) inhibitors, must be evaluated to optimize motor symptom control. Clinical experience suggests that early diagnosis and treatment initiation may improve the outcome. Additionally, a multidisciplinary treatment approach should be utilized to monitor neurocognitive development and other comorbidities that may occur in THD. In this consensus guideline, representatives of the International Working Group on Neurotransmitter related Disorders (iNTD) and patient advocates evaluated all the evidence available in the literature on the diagnosis and management of THD and developed recommendations using the SIGN and GRADE methodologies. Based on the limited evidence, practical recommendations have been developed to support clinical diagnosis, laboratory testing, neuroimaging, medical treatment, and non-medical interventions. Research topics for further development were identified. This guideline aims to improve the care of patients with THD worldwide and raise general awareness of this rare disease.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".