High Rates of Psychiatric Comorbidity in Narcolepsy
Bibliographic record
Abstract
OBJECTIVE: To evaluate psychiatric comorbidity patterns in patients with a narcolepsy diagnosis in the United States. METHODS: Truven Health Analytics MarketScan Research Databases were accessed to identify individuals ≥ 18 years of age with ≥ 1 ICD-9 diagnosis code(s) for narcolepsy continuously insured between 2006 and 2010 and non-narcolepsy controls matched 5:1 (age, gender, region, payer). Extensive subanalyses were conducted to confirm the validity of narcolepsy definitions. Narcolepsy subjects and controls were compared for frequency of psychiatric comorbid conditions (based on ICD-9 codes/Clinical Classification Software [CCS] level 2 categories) and psychiatric medication use. RESULTS: The final population included 9,312 narcolepsy subjects and 46,559 controls (each group, mean age = 46.1 years; 59% female). All categories of mental illness were significantly more prevalent in patients with narcolepsy versus controls, with the highest excess prevalence noted for CCS 5.8 Mood disorders (37.9% vs 13.8%; odds ratio [OR] = 4.0; 95% CI, 3.8-4.2), CCS 5.8.2 Depressive disorders (35.8% vs 13.0%; OR = 3.9; 95% CI, 3.7-4.1), and CCS 5.2 Anxiety disorders (25.1% vs 11.9%; OR = 2.5; 95% CI, 2.4-2.7). Excess prevalence of anxiety and mood disorders (narcolepsy vs controls) was higher in younger age groups versus older age groups. Psychiatric medication usage was higher in the narcolepsy group versus controls in the following categories: selective serotonin reuptake inhibitors (36% vs 17%), anxiolytic benzodiazepines (34% vs 19%), hypnotics (29% vs 13%), serotonin-norepinephrine reuptake inhibitors (21% vs 6%), and tricyclic antidepressants (13% vs 4%) (all P values < .0001). CONCLUSIONS: Narcolepsy is associated with significant comorbid psychiatric illness burden and higher psychiatric medication usage compared with the non-narcolepsy population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".