From Hurt to Harm: How Childhood Trauma Shapes Cognitive Control and Decision Making in Late-Life Depression
Bibliographic record
Abstract
Objective: To investigate the neuropsychological component features of elderly patients with a history of late-life depression reporting a previous history of Childhood Trauma (CT). Methods: Outpatients between 60 and 85 years old with a recent history of late-life depression were divided into two groups, childhood trauma and no childhood trauma, according to a previous history of childhood trauma assessed with the Childhood Trauma Questionnaire (CTQ). Cognitive abilities were assessed using the stroop color and word test (inhibition), iowa gambling task (decision-making), and verbal fluency test (semantic verbal fluency). Results: Total CTQ scores were associated with lower stroop color and word test time scores (p=0.041). Physical Abuse (PA) was associated with a lower Iowa Gambling Task (IGT) net score (p=0.015). Emotional Neglect (EN) was associated with a higher semantic verbal fluency score (p=0.021) and a lower stroop test time score (p=0.037). Emotional abuse, sexual abuse and physical neglect had no neuropsychological pattern. These results remained significant even after controlling for confounding factors including age, gender, level of depression, antidepressant treatment, and history of previous suicide attempts. Conclusion: Patients with a history of physical abuse had low decision-making scores and those with a history of emotional neglect had good cognitive control. It is necessary to examine how neurocognitive mechanisms are impacted by childhood traumas to develop therapeutic intervensions that improve cognitive performance in older adults. Keywords Child abuse; CTQ; Late-life depression; Stroop; Decision-making
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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 | 0.002 |
| 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 | 0.001 | 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".