A retrospective analysis of clinical characteristics of childhood narcolepsy
Bibliographic record
Abstract
AIM: Narcolepsy, encompassing excessive daytime sleepiness (EDS), cataplexy, sleep paralysis and hypnogogic hallucinations, was previously considered rare in childhood. Recently, cases of childhood narcolepsy have increased significantly and the reasons for this may include the increasing awareness of narcolepsy as well as the H1N1 vaccination. The aim of this study was to describe the clinical characteristics of childhood narcolepsy, specifically focusing on cataplexy subtypes that may facilitate early recognition of narcolepsy. METHODS: We retrospectively reviewed and analyzed the medical records of 33 children diagnosed with narcolepsy at the Hospital for Sick Children, in Toronto, Ontario. All patients were seen prior to 18 years of age and symptoms were self-reported by parents and/or children themselves. RESULTS: At presentation, 32 of 33 children reported EDS and 28 of 33 reported cataplexy. Among the 28 patients with cataplexy, 18 of 28 reported cataplexy referred to as 'cataplectic facies' (e.g., facial hypotonia and/or tongue protrusion) while 10 of 28 patients reported characteristic cataplexy, defined as bilateral loss of muscle tone. Children with cataplectic facies reported higher BMI z-scores compared to those with characteristic cataplexy, 1.8 and 0.8, respectively. Children with cataplectic facies also tended to be younger than those with characteristic cataplexy, 9.2 and 11.8 years of age, respectively. Cataplectic facies appear to be related to narcolepsy close to disease onset. CONCLUSIONS: Children, especially young, obese children, presenting with a history of EDS with associated facial hypotonia or tongue protrusion raises the index of suspicion of narcolepsy and should prompt a referral to a specialized sleep facility to establish the diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".