Secondary Abnormal CSF Neurotransmitter Metabolite Profiles in a Pediatric Tertiary Care Centre
Bibliographic record
Abstract
BACKGROUND: Secondary neurotransmitter deficiencies have been reported in several reviews. Our primary aim was to assess the relationship among epilepsy, antiseizure medications, and specific neurotransmitter abnormalities. We also evaluated movement disorders and brain abnormalities via magnetic resonance imaging scans in patients with secondary neurotransmitter defects. METHODS: This is a retrospective case series of 376 patients who underwent neurotransmitter analysis at BC Children's Hospital between 2009 and 2013, for a variety of neurological presentations. The biochemical genetics laboratory database was interrogated for results of cerebrospinal fluid neurotransmitter analyses. Clinical data for patients with abnormal results were collected from the hospital charts. Statistical analysis included one-way analysis of variance, chi-square, and a two-way contingency table. RESULTS: Abnormal neurotransmitter values were identified in 67 (17.8%) patients, two (0.53%) of which were attributable to a congenital neurotransmitter disorder and 11 (16.9%) secondary to other genetic diagnoses. Of 64 patients with secondary abnormal neurotransmitter values, 38 (59%) presented with epilepsy and 20 (31%) with movement disorders. A combination of epilepsy and movement disorder was less frequent. DISCUSSION: Acknowledging the limitations of this retrospective chart review, we conclude that, in our cohort, in addition to patients with movement disorders, a considerable number of patients with epilepsy and epileptic encephalopathy also showed secondary neurotransmitter mono-amine abnormalities. There is no clear relation, however, between clinical phenotype and type of neurotransmitter affected. In addition, no association was identified between the type of antiseizure medications and affected neurotransmitter type. We outline the need for prospective studies to further enrich our understanding of the relation between epilepsy and neurotransmitters with a focus on improving treatments and patient outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".