Saccadic Eye Movements in Elderly Depressed Patients With Suicidal Behaviors: An Exploratory Eye-Tracking Study
Bibliographic record
Abstract
Suicidal behaviors (SBs) are often associated with impaired performance on neuropsychological executive functioning (EF) measures that encourage the development of more specific and reliable tools. Recent evidence could suggest that saccadic movement using eye tracking can provide reliable information on EF in depressive elderly. The aim of this study was to describe oculomotor performances in elderly depressed patients with SB. To achieve this aim, we compared saccadic eye movement (SEM) performances in elderly depressed patients (N = 24) with SB and with no SB in prosaccade (PS) and antisaccade (AS) tasks under the gap, step, and overlap conditions. All participants also underwent a complete neuropsychological battery. Performances were impaired in patients with SB who exhibited less corrected AS errors and longer time to correct them than patients with no SB. Moreover, both groups had a similar performance for PS latencies and correct AS. These preliminary results suggested higher cognitive inflexibility in suicidal patients compared to non-suicidal. This inflexibility may explain the difficulty of the depressed elderly in generating solutions to the resurgence of suicidal ideation (SI) to respond adequately to stressful environments. The assessment of eye movement parameters in depressed elderly patients may be a first step in identifying high-risk patients for suicide.
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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 | 0.001 | 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".