Complementary medicine for children and young people who have attention deficit hyperactivity disorder
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite effectiveness of medication in treating children and young people who have attention deficit hyperactivity disorder (ADHD), concerns about the effects of medication on children's developing brains, adverse side-effects, possibility of long-term use, and compliance issues have all contributed to the continuing search for alternative therapies. This article reviews the latest scientific evidence of the effectiveness and safety of these treatments in ADHD. RECENT FINDINGS: Although there is evidence from a large randomized controlled study that neurofeedback has positive effects on reducing children's symptoms of ADHD, most recent randomized controlled trials have generally yielded negative results. Some positive results exist from a pilot study of repetitive transcranial magnetic stimulation. However, the sample size was far too small to enable any conclusions to be drawn about the evidence. Findings from the recent randomized controlled trials of supplements of essential fatty acids in children who have ADHD clearly demonstrated lack of superiority compared with placebo. SUMMARY: Notwithstanding efforts made to increase the scientific rigor of previous studies, more recent studies have generally been unsuccessful in demonstrating adequate treatment effects of complementary medicine on children who have ADHD. Currently, there is no proof that complementary medicine provides a better alternative for children who have ADHD than treatments that are currently available within multimodal therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".