Understanding attention deficit hyperactivity disorder as a continuum.
Bibliographic record
Abstract
OBJECTIVE: To review research findings that consider whether attention deficit hyperactivity disorder (ADHD) is a discrete entity or whether it is more consistent with an extreme end-of-trait distribution in the population and to then grapple with the potential clinical implications. QUALITY OF EVIDENCE: Peer-reviewed publications in the past 5 years, drawing from diverse fields (taxonomy, epidemiology, genetics, neurobiology, and neuropsychology), were identified through searches in MEDLINE and PsycINFO. MAIN MESSAGE: Accumulating research findings are most consistent with a predominately dimensional rather than a qualitatively distinct existence for ADHD. This does not negate the clinical needs of those who have substantial ADHD symptom clusters, nor the risks that such symptoms entail. However, the lack of discontinuity in the distribution of such traits in the population creates great uncertainty as to what thresholds should prompt explicit intervention. CONCLUSION: The implications of this pattern of findings might include the need to de-emphasize categorical conceptualizations of ADHD, produce evidence to better inform risk-benefit ratios of interventions along a spectrum of symptom and functional severity, and more coherently triage and arrange service delivery on the basis of symptom and functional severity rather than artificial diagnostic categorizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".