Meta‐analysis of the association between tryptophan hydroxylase and suicidal behavior
Bibliographic record
Abstract
Tryptophan hydroxylase (TPH) has been the candidate gene of focus in many of the association studies of suicidal behavior in recent years. Initial positive findings with respect to an association between the TPH gene and suicidal behavior have been replicated, but not consistently. Typically, individual studies have investigated small samples, and thus they repeatedly had insufficient statistical power to detect a positive association. Meta-analysis is one approach that can be used to achieve greater statistical power and may be helpful in providing a more conclusive understanding. We used meta-analytic techniques to investigate the association between an intron 7 polymorphism in the TPH gene and suicidal behavior. A total of 39 publications were identified and reviewed, 17 of which were selected for inclusion in this study. We performed two meta-analyses. One compared suicide attempters or completers (N = 1,290) with healthy controls (N = 2,295); the other compared suicide attempters (N = 625) with nonattempters (N = 1,475). None of these studies provided evidence for association (odds ratio (OR) = 1.14, 95% confidence interval (CI) = 0.97-1.34 for the former and OR = 0.96, 95% CI = 0.77-1.20 for the latter). The combined results from comparisons within both groups showed no overall association between suicidal behavior and an intron 7 polymorphism of the TPH gene.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.002 | 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".