Training for Success in a Child with ADHD
Bibliographic record
Abstract
This article presents a case study to illustrate how children with attention-deficit/hyperactivity disorder (ADHD) can be assessed and successfully trained using neurofeedback. There is established efficacy for using neurofeedback to treat ADHD (Arns, De Ridder, Strehl, Breteler, & Coenen, 2009; Gani, Birbaumer, & Strehl, 2009; Gevensleben et al., 2009). Indeed, the American Academy of Pediatrics gave biofeedback Level 1 efficacy in its 2012 review (American Academy of Pediatrics, 2012), the same level of efficacy as is given to medications. The other condition that has sufficient randomized controlled studies to establish efficacy for electroencephalogram biofeedback is epilepsy (Tan et al., 2009). This case is presented to share techniques that will help clinicians conduct neurofeedback appropriately so that good results are obtained. The future of our field depends on every practitioner doing a quality job with excellent outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".