Urine creatine metabolite panel as a screening test in neurodevelopmental disorders
Bibliographic record
Abstract
BACKGROUND: Cerebral creatine deficiency disorders (CCDD) are inherited metabolic disorders of creatine synthesis and transport. Urine creatine metabolite panel is helpful to identify these disorders. METHODS: We reviewed electronic patient charts for all patients that underwent urine creatine metabolite panel testing in the metabolic laboratory at our institution. RESULTS: There were 498 tests conducted on 413 patients. Clinical, molecular genetics and neuroimaging features were available in 318 patients. Two new patients were diagnosed with creatine transporter deficiency: one female and one male, both had markedly elevated urine creatine. Urine creatine metabolite panel was also used as a monitoring test in our metabolic laboratory. Diagnostic yield of urine creatine metabolite panel was 0.67% (2/297). There were six known patients with creatine transporter deficiency. The prevalence of creatine transporter deficiency was 2.64% in our study in patients with neurodevelopmental disorders who underwent screening or monitoring of CCDS at our institution. CONCLUSION: Even though the diagnostic yield of urine creatine metabolite panel is low, it can successfully detect CCDD patients, despite many neurodevelopmental disorders are not a result of CCDD. To the best of our knowledge, this study is the first Canadian study to report diagnostic yield of urine creatine metabolite panel for CCDD from a single center.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".