Are there associations between Executive Functions and Theory of Mind in attention deficit hyperactivity disorder? Results from a systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: Deficits in Executive Function (EF) and Theory of Mind (ToM) are common and significant in attention deficit hyperactivity disorder (ADHD), impacting self-regulation and social interaction. The nature of ToM deficits is believed to be partially associated with preexisting deficits in other core cognitive domains of ADHD, such as EF, which are essential for making mental inferences, especially complex ones. Evaluating these associations at a meta-analytic level is relevant. OBJECTIVE: To conduct a systematic literature review followed by a meta-analysis to identify potential associations between EF and ToM among individuals with ADHD and their healthy counterparts, considering different developmental stages. METHOD: A systematic review was conducted in seven different databases. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale. The meta-analytic measurement was estimated with the correlation coefficient as the outcome. Due to the presence of heterogeneity, a random-effects model was adopted. Independent meta-analyses were conducted for different EF subdomains and ADHD and healthy control groups. Subgroup analyses were performed to examine the influence of age on the outcome of interest. RESULTS: > 0.20). CONCLUSION: The association between EF and ToM was significant, with a moderate effect size, although no significant differences were found according to age, the presence of ADHD, or EF subdomains. Future research is suggested to expand the age groups and overcome the methodological limitations indicated in this review.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".