Auditory mismatch negativity in attention deficit hyperactivity disorder in children： a Meta-analysis
Bibliographic record
Abstract
ObjectiveTo explore the differences existing in the auditory mismatch negativity （MMN） amplitude and latency between children with attention deficit hyperactivity disorder （ADHD） and normal children， and to probe into the significance of MMN latency and amplitude for assessing the auditory perception and attention level in ADHD children and normal children.MethodsOn December 1， 2022， a systematic search was performed in PubMed， Embase， Cochrane Library， China National knowledge Infrastructure （CNKI）， Wanfang Data Knowledge Service Platform and VIP databases to identify all well qualified literature focusing on MMN of ADHD children， then the valid data relevant to MMN amplitude and latency were extracted. The Newcastle-Ottawa Scale （NOS） was used to assess the quality of the included studies， and Stata 20.0 was employed for Meta-analysis.ResultsA total of 9 qualified studies comparing ADHD children （n=170） against healthy controls （n=159） were finally included. Among the included literature， there were 18 matched pairs of MMN amplitude data and 10 matched pairs of MMN latency data at different recording sites. Meta-analysis denoted that ADHD group resulted in potentials of slightly lower MMN amplitude （WMD=-0.334， 95% CI： -1.426~0.758， P=0.549） and notably longer MMN latency （WMD=14.768， 95% CI： 4.660~24.876， P=0.004） compared to control group， and the Bgger's funnel plot did not reveal any publication bias.ConclusionCompared with healthy controls， ADHD children have longer MMN latency， suggesting that the auditory perception and attention level of ADHD children may be reduced.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.042 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".