Influence of sex on tic severity and psychiatric comorbidity profile in patients with pediatric tic disorder
Bibliographic record
Abstract
AIM: To investigate sex-related differences in tic severity, tic-related impairments, and psychiatric comorbidities in childhood. METHOD: In this cross-sectional study, tic severity/impairment and demographic factors were collected from 270 children and young people (aged 5-17y, mean 10y 6mo, SD 3y 4mo; 212 males and 58 females) with a tic disorder diagnosis at a specialty clinic. Psychiatric diagnoses and corresponding screening questionnaire scores were collected for attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder, and anxiety disorders. Logistic regression was used to compare the effect of sex and age on psychiatric comorbid diagnoses. The Mann-Whitney U test and t-tests were used to assess differences in questionnaire score distribution between sexes. RESULTS: Females had more severe motor tics (12.55 vs 10.81, p=0.01) and higher global severity scores (38.79 vs 32.66, p=0.03) on the Yale Global Tic Severity Scale. Females were less likely to be diagnosed with ADHD (odds ratio=0.48, 95% confidence interval=0.26-0.89). No significant sex difference was observed in diagnosis rates or symptom severity scores for anxiety or OCD. Females had significantly higher scores than males on the Children's Depression Inventory, Second Edition. INTERPRETATION: The higher level of motor tic severity and global severity in females further supports the differential natural history of tic disorders in females. Females with tic disorders may be underdiagnosed for ADHD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".