Influence of Stress Factors on the Development of Post-Traumatic Stress Disorder in Children: Risk Factors
Bibliographic record
Abstract
In the current conditions of the ongoing war in Ukraine, which began in February 2022, Ukrainian children might develop post-traumatic stress disorder (PTSD) due to stress factors caused by the conflict between Ukraine and Russian-backed separatist forces, along with the Russian military. In particular, the relationship between reduced emotional intelligence and the development of post-traumatic stress disorder in children has become more relevant. This study aimed to assess structural and cognitive changes in children with PTSD and their relationship to depression, anxiety, and event segmentation. The study methods included clinical interviews (CAPS-CA-5 scale), neuropsychological tests (short-term and long-term memory tests), self-assessment questionnaires (standardised CDI, RCADS and SCAS-Child scales), and a single-shotMRI. The results showed that patients with post-traumatic stress disorder had reduced hippocampal volume (p=0.018) and the volume of cingulate cortex isthmus (p=0.026). Diffusion in the cerebellum-hippocampal tract was reduced (p=0.014). The level of depression was positively correlated with hippocampal volume (r=0.32, p=0.021) and anxiety with the volume of cingulate cortex isthmus (r=0.26, p=0.048). These results emphasise the importance of the relationship between structural changes and levels of depression and anxiety in patients with PTSD. Prospects for further research are based on the study of the long-term effects of psychotherapeutic interventions aimed at improving cognitive function in patients with posttraumatic stress disorder.
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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) | 0.002 | 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".