A meta-analysis of neuropsychological markers of vulnerability to suicidal behavior in mood disorders
Bibliographic record
Abstract
BACKGROUND: Suicidal behavior results from a complex interplay between stressful events and vulnerability factors, including cognitive deficits. However, it is not clear which cognitive tests may best reveal this vulnerability. The objective was to identify neuropsychological tests of vulnerability to suicidal acts in patients with mood disorders. METHOD: A search was made of Medline, EMBASE and PsycINFO databases, and article references. A total of 25 studies (2323 participants) met the selection criteria. A total of seven neuropsychological tests [Iowa gambling task (IGT), Stroop test, trail making test part B, Wisconsin card sorting test, category and semantic verbal fluencies, and continuous performance test] were used in at least three studies to be analysed. RESULTS: IGT and category verbal fluency performances were lower in suicide attempters than in patient controls [respectively, g = -0.47, 95% confidence interval (CI) -0.65 to -0.29 and g = -0.32, 95% CI -0.60 to -0.04] and healthy controls, with no difference between the last two groups. Stroop performance was lower in suicide attempters than in patient controls (g = 0.37, 95% CI 0.10-0.63) and healthy controls, with patient controls scoring lower than healthy controls. The four other tests were altered in both patient groups versus healthy controls but did not differ between patient groups. CONCLUSIONS: Deficits in decision-making, category verbal fluency and the Stroop interference test were associated with histories of suicidal behavior in patients with mood disorders. Altered value-based and cognitive control processes may be important factors of suicidal vulnerability. These tests may also have the potential of guiding therapeutic interventions and becoming part of future systematic assessment of suicide risk.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".