Biederman’s Contribution to the Understanding of Executive Function in ADHD
Bibliographic record
Abstract
OBJECTIVE: To examine the theoretical and empirical contribution of Joe Biederman and his colleagues to the understanding of executive function (EF) and ADHD. METHOD: We searched PubMed for references to EF in Biederman's publications and conducted a narrative review of this literature. RESULTS: In 50 or more papers using neuropsychological tests, rating scales and measures of mind wandering, Biederman demonstrated that EF are evident in ADHD and closely linked to its underlying neurobiological and genetic risk. He argued that EF need to be monitoring to ensure comprehensive assessment and treatment, but could not be used as a diagnostic proxy. CONCLUSION: Biederman built an innovative and impressive collaboration to address the issue of EF in ADHD. His work shows a commitment to understanding of EF in order to improve patient care. Biederman laid down a roadmap for research in ADHD and EF for the rest of the field to follow.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".