Management and Outcomes of Very Long-Chain Acyl-CoA Dehydrogenase Deficiency (VLCAD Deficiency): A Retrospective Chart Review
Bibliographic record
Abstract
Very long-chain acyl-CoA dehydrogenase (VLCAD) deficiency is a rare genetic condition affecting the mitochondrial beta-oxidation of long-chain fatty acids. This study reports on the clinical outcomes of patients diagnosed by newborn screening with VLCAD deficiency comparing metabolic parameters, enzyme activities, molecular results, and clinical management. It is a single-center retrospective chart review of VLCAD deficiency patients who met the inclusion criteria between January 2002 and February 2020. The study included 12 patients, 7 of whom had an enzyme activity of more than 10%, and 5 patients had an enzyme activity of less than 10%. The Pearson correlation between enzyme activity and the C14:1 level at newborn screening showed a p-value of 0.0003, and the correlation between enzyme activity and the C14:1 level at diagnosis had a p-value of 0.0295. There was no clear correlation between the number of documented admissions and the enzyme activity level. Patients who had a high C14:1 value at diagnosis were started on a diet with a lower percentage of energy from long-chain triglycerides. The C14:1 result at diagnosis is the value that has been guiding our initial clinical management in asymptomatic diagnosed newborns. However, the newborn screening C14:1 value is the most sensitive predictor of low enzyme activity and may help guide dietary management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".