While the molecular basis receives attention, development of a molecular-based diagnosis is still in a deficit: understanding Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
Attention deficit hyperactivity disorder is the most prevalent childhood-onset behavioral disorder, affecting approximately 8% of the population, where a disproportionate amount of males are afflicted. Common symptoms of the disorder include inattentiveness, hyperactivity, and impulsivity. Genomewide linkage analyses have demonstrated that the disorder is likely due to several genetic factors, whereby the dopaminergic, serotonergic, and noradrenergic neurotransmitter systems are highly implicated through various observations. Genetic screens of afflicted individuals have implicated the presence of specific genetic polymorphisms with ADHD, examples being the 10-repeat-40-base-pair allele of the dopamine transporter, DAT-1, and the silent-G861C-substitution allele of the serotonin receptor, 5-HT1B. Evidence is emerging that proteins involved in the release of neurotransmitters from synaptic vesicles, like SNAP-25, may also be involved in the pathology of ADHD. The most common method of treatment is the administration of psychostimulants, like amphetamine derivatives and methylphenidate (Ritalin®), drugs which target the dopaminergic system. New therapies that target other neurotransmitter systems, like the selective noradrenaline transport inhibitor, atomoxetine, are gaining recognition as effective treatments. Common methods to diagnose ADHD reside in psychological assessments. As more insight is gained into the genetic basis for the disorder, it appears likely that a clinical diagnostic test based on genetic screening for these factors, such as specific genetic polymorphisms, could serve as an additional means of diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".