Effects of Neurofeedback Training on Inhibitory Capacities in ADHD Children: A Single-Blind, Randomized, Placebo-Controlled Study
Bibliographic record
Abstract
Introduction.Studies performed during the last decades suggest that neurofeedback (NF) training can effectively reduce symptomatology in children with Attention deficit hyperactivity disorder (ADHD).Yet questions remain concerning specific effects of NF training in ADHD children, because these studies did not use a randomized, placebo-controlled approach.To address this issue, such an approach was used in the present study to measure the impact of NF training on inhibitory capacities.Method.Nine ADHD children (with no comorbidity), aged 8 to 13 years, were randomly assigned to either an experimental group (n ¼ 5) or a placebo group (n ¼ 4).For both groups, training protocols comprised 40 one-hr sessions (20 meetings of 2 sessions each).Sensorimotor rhythm=Theta training was used in the experimental group.Prerecorded sessions of the first author's EEG activity were used in the placebo group.Pre-and posttraining assessments consisted of the Conner's Parent Rating Scales (CPRS-R) and neuropsychological tests.A multiple case study strategy was applied for data analysis using a Reliable Change Index when applicable.Results.One experimental participant was a dropout, and one placebo participant had to be discontinued due to adverse effects.The latter participant accepted to undergo posttraining evaluations; hence an Intention-to-Treat analysis was performed on this participant's data.Remaining participants showed significant improvements on the CPRS-R.Improvements were measured on the Variability measure of the CPT-II consistently across the placebo group and
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".