<scp>SWAN</scp> scale for <scp>ADHD</scp> trait‐based genetic research: a validity and polygenic risk study
Bibliographic record
Abstract
BACKGROUND: Population-based samples with valid, quantitative and genetically informative trait measures of psychopathology could be a powerful complement to case/control genetic designs. We report the convergent and predictive validity of the parent- and self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN). We tested if SWAN scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as traits and polygenic risk for disorders that co-occur with ADHD: anxiety and obsessive-compulsive disorder (OCD). METHODS: We collected parent- and self-report SWAN scores in a sample of 15,560 children and adolescents (6-17 years) recruited at a science museum (Spit for Science sample). We established age and sex norms for the SWAN. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with and without a reported ADHD diagnosis. These cut-points were validated in a clinic sample (266 ADHD cases; 36 controls). Convergent validity was established using the Conners' parent- and self-report scales. Using Spit for Science participants with genome-wide data (n = 5,154), we tested if low, medium and high SWAN scores were associated with polygenic risk for ADHD, OCD and anxiety disorders. RESULTS: Parent- and self-report SWAN scores showed high convergent validity with Conners' scales and distinguished ADHD participants with high sensitivity and specificity in the Spit for Science sample. In a clinic sample, the Spit for Science cut-points discriminated ADHD cases from controls with a sensitivity of 84% and specificity of 92%. High SWAN scores and scores above the Spit for Science cut-points were significantly associated with polygenic risk for ADHD. SWAN scores were not associated with polygenic risk for OCD or anxiety disorders. CONCLUSIONS: Our study supports the validity of the parent- and self-report SWAN scales and their potential in ADHD population-based genetic research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".