Correlation investigation between BDNF (Val66Met/rs6265) polymorphism and attention deficit hyperactivity disorder susceptibility in Chinese mainland population: a meta-analysis
Bibliographic record
Abstract
This meta-analysis evaluated the association between the Val66Met polymorphism of brain-derived neurotrophic factor (BDNF) and the susceptibility to attention deficit hyperactivity disorder (ADHD) in the Chinese mainland population. Eligible documents were selected from online databases including PubMed, Embase, Cochrane Library, CNKI, Wanfang and CBM (updated to 15 October 2023). The evaluation of study quality was conducted according to guidelines of Newcastle-Ottawa Scale. Basic features of patients, OR and 95% CI were retrieved to assess the correlation between ADHD susceptibility and Val66Met polymorphism in four genetic models: allele genetic model (mutation (A) vs. wild-type (G)), additive genetic model (AA vs. GG and AG vs. GG), recessive genetic model (AA vs. AG+GG) and dominant genetic model (AA+AG vs. GG). This study included totally four studies for subsequent meta-analysis. The results indicated that the correlation between ADHD susceptibility and Val66Met polymorphism in A vs. G (OR = 0.8840, 95%CI: [0.6696–1.1672], p = 0.3846), AA vs. GG (OR = 0.8436, 95%CI: [0.5432–1.3102], p = 0.4490), AA+AG vs. GG (OR = 0.8602, 95%CI: [0.6497–1.1391], p = 0.2933), AG vs. GG (OR = 0.9132, 95%CI: [0.7810–1.0679], p = 0.2556) and AA vs. GG+AG (OR = 1.0315, 95%CI: [0.8789–1.2105], p = 0.7044) was not significant. Egger’s test and sensitivity analysis demonstrating the reliability and stability our conclusions, respectively. BDNF Val66Met polymorphism did not contribute to the susceptibility of ADHD in Chinese mainland population.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".