Active and Passive Attentional Processing in Adolescent Suicide Attempters: An Event-Related Potential Study
Bibliographic record
Abstract
INTRODUCTION: Suicide is the second leading cause of death among adolescents. Suicidal behavior is associated with impairments in attention. Attention can be directed toward relevant events in the environment either actively, under voluntary control, or passively, by external salient events. The extent to which the risk for suicidal behavior affects active and passive attention is largely unknown. METHODS: Event-related potentials (ERPs) were recorded while 14 adolescents with acute suicidal behavior and 14 healthy controls performed an auditory 3-stimulus oddball task. The task consisted of standard (80%), target (10%), and novel (10%) stimuli. The participants were instructed to press a button upon presentation of the target. The novel stimuli were unexpected and irrelevant to the target detection task. RESULTS: Accuracy of target detection was slightly but significantly reduced in the suicidal group. There were no significant differences in the amplitude of the target-N2 or -P3b between groups. There was a slight, but nonsignificant, increase in the amplitude of the novel-N2 and -P3 in the suicidal group. CONCLUSIONS: This is the first study to explore both passive and controlled aspects of attention using ERPs in adolescents with acute suicidal behavior. Although there were no significant ERP group differences, this is an important step in identifying objective markers of suicide risk among adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".