Cognitive remediation of executive functioning in youth with neuropsychiatric conditions: current knowledge on feasibility, effectiveness, and personalization
Bibliographic record
Abstract
Introduction: Cognitive remediation is an intervention aimed at improving cognitive functioning. Executive functions are higher-order cognitive processes aimed to manage personal resources to achieve a goal, which are often impaired in neuropsychiatric disorders.Areas covered: We review the literature on cognitive remediation of executive functioning in youth with neuropsychiatric conditions including schizophrenia and psychosis, autism spectrum disorders (ASD), anorexia nervosa, anxiety disorders, and depressive disorders. We identify potential factors associated with treatment response, discuss treatment personalization, and suggest ways to improve personalization. This review suggests cognitive remediation is well accepted by patients and families and can be successfully delivered. Consistent evidence suggests attention bias modification is an effective intervention in anxiety disorders. A few programs were also effective in youth with ASD and schizophrenia. However, findings are mixed regarding other forms of intervention. Several treatment-level and individual factors may impact outcomes.Expert commentary: Treatment personalization seems to be particularly relevant and must be considered by clinicians when planning interventions. Treatment parameters should be selected based on individual needs and capacities. More studies are needed on treatment effectiveness, and to improve programs, extend findings to other neuropsychiatric subgroups, and clarify the role of potential predictors of treatment response.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".