Dopamine genes and ADHD
Bibliographic record
Abstract
Family, twin, and adoption studies have documented a strong genetic basis for ADHD/HKD, but these studies do not identify specific genes linked to the disorder. Molecular genetic studies can identify allelic variations of specific genes that are functionally associated with ADHD/HKD, and dopamine genes have been the initial candidates based on the site of action of the stimulants drugs, which for a half century have provided the primary pharmacological treatment for ADHD/HKD. Two candidate dopamine genes have been investigated and reported to be associated with ADHD/HKD: the dopamine transporter (DAT1) gene [Cook et al., American Journal of Human Genetics 1995;56:993-998, Gill et al., Molecular Psychiatry 1997;2:311-313] and the dopamine receptor D4 (DRD4) gene [LaHoste et al., Molecular Psychiatry 1996;1:121-124: Smalley et al., 1998;3:427-430; Swanson et al., Molecular Psychiatry 1998;3:38-41]. Speculative hypotheses [Swanson and Castellanos, NIH Consensus Development Conference: Diagnosis and Treatment of Attention Deficit Hyperactivity Disorder, November 1998. p. 37-42] have suggested that specific alleles of these dopamine genes may alter dopamine transmission in the neural networks implicated in ADHD/HKD (e.g. that the 10-repeat allele of the DAT1 gene may be associated with hyperactive re-uptake of dopamine or that the 7-repeat allele of the DRD4 gene may be associated with a subsensitive postsynaptic receptor). These and other variants of the dopamine hypothesis of ADHD will be 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".