Half a century of research on Attention-Deficit/Hyperactivity Disorder: A scientometric study
Bibliographic record
Abstract
We performed a scientometric analysis of the scientific literature on ADHD to evaluate key themes and trends over the past decades, informing future lines of research. We conducted a systematic search in Web of Science Core Collection up to 15 November, 2021 for scientific publications on ADHD. We retrieved 28,381 publications. We identified four major research trends: 1) ADHD treatment, risks factors and evidence synthesis; 2) neurophysiology, neuropsychology and neuroimaging; 3) genetics; 4) comorbidity. In chronological order, identified clusters of themes included: tricyclic antidepressants, ADHD diagnosis/treatment, bipolar disorder, EEG, polymorphisms, sleep, executive functions, pharmacology, genetics, environmental risk factors, emotional dysregulation, neuroimaging, non-pharmacological interventions, default mode network, Tourette, polygenic risk score, sluggish cognitive tempo, evidence-synthesis, toxins/chemicals, psychoneuroimmunology, Covid-19, and physical exercise. In conclusion, research on ADHD over the past decades has been driven mainly by a medical model. Whereas the neurobiological correlates of ADHD are undeniable and crucial, we look forward to further research on relevant psychosocial aspects related to ADHD, such as societal pressure, the concept of neurodiversity, and stigma.
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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 | 0.028 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.151 | 0.196 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".