Cognitive Inhibition in Elderly High-lethality Suicide Attempters
Bibliographic record
Abstract
People who attempt suicide often display cognitive impairments, particularly poor cognitive control. Could poor cognitive control contribute to high suicide rates in old age? A component of cognitive control, cognitive inhibition – active suppression of task-irrelevant processing – is very sensitive to aging and has been linked to attempted suicide. We investigated cognitive inhibition in older high-lethality suicide attempters, closely resembling suicide victims, as well as low-lethality attempters, and control groups with and without depression and suicidal ideation. 102 participants aged 60+ (17 psychiatrically healthy control subjects, 38 depressed control subjects, 16 suicide ideators, 14 low-lethality suicide attempters, and 17 high-lethality suicide attempters) underwent comprehensive clinical and cognitive assessments. They completed the Delis–Kaplan Executive Function System Color-Word Interference Test, a validated modification of the Stroop test. High-lethality suicide attempters demonstrated a distinct pattern of cognitive inhibition deficits. Compared to psychiatrically healthy control subjects and non-suicidal depressed patients, high-lethality attempters took longer to complete inhibition trials, even after accounting for potential confounding factors (age, education, MMSE score, information processing speed, and accuracy). Compared to non-suicidal depressed and healthy control subjects, low-lethality suicide attempters committed more uncorrected errors; however, this difference was not specific to the inhibition condition. Older suicide attempters are a cognitively heterogeneous group. Poor cognitive control in high-lethality attempters may undermine their ability to solve real-life problems, precipitating a catastrophic accumulation of stressors. Meanwhile, low-lethality attempters’ poor performance may reflect a careless approach to the task or faulty monitoring. Download : Download full-size image
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".