Relations between multi-informant assessments of ADHD symptoms, DAT1, and DRD4.
Bibliographic record
Abstract
Researchers conducting candidate gene studies of attention-deficit/hyperactivity disorder (ADHD) typically obtain symptom ratings from multiple informants (i.e., mothers, fathers, and teachers) and use a psychologist's best estimate or a simple algorithm, such as taking the highest symptom ratings across informants, to construct diagnostic phenotypes for estimating association. Nonetheless, these methods have never been empirically validated in the context of a molecular genetic study. In the current study, the authors systematically evaluated several methods of operationalizing phenotypes and the resulting evidence for association between ADHD and the candidate genes: dopamine transporter gene (DAT1) and dopamine D4 receptor gene (DRD4). Use of symptom scores as continuous scales in regression analysis suggested that the combination of mother and teacher ratings yielded the strongest evidence for association between hyperactive-impulsive ADHD symptoms and DAT1 and between inattentive ADHD symptoms and DRD4. Teacher ratings alone were sufficient for evaluating the association between inattentive symptoms and DAT1. Further, this regression-based method consistently yielded stronger evidence for association among ADHD symptoms, DAT1, and DRD4 than did three simple algorithms (i.e., the and, or, and averaging rules). The implications of these results for future molecular genetic studies of ADHD are discussed.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".