P01-177 - Peritraumatic Reactions and Post-traumatic Stress Symptoms in School-aged Children Victims of Road Traffic Accident
Bibliographic record
Abstract
Objective It remains unknown whether peritraumatic reactions predict PTSD symptoms in younger populations. To prospectively investigated the power of self-reported peritraumatic distress and dissociation to predict the development of PTSD symptoms at 1-month in school-aged children. Methods A sample of 103 school-aged children (8-15 years old) admitted to an Emergency Department after a road traffic accident were consecutively enrolled. Peritraumatic distress was assessed using the Peritraumatic Distress Inventory (range 0-52) and peritraumatic dissociation was assessed using the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) (range 10-50). PTSD symptoms were measured at 1-month by both the child version of the clinician-administered PTSD Scale (CAPS-CA) (range: 0-136) and the Child Post-traumatic Stress Reaction Index (CPTS-RI) (range 0-80). Results Mean(SD) participants’ age was 11.7(2.2) and 53.4% (n=55) of them were of male gender. At baseline, mean PDI and PDEQ scores were 21.4 (SD=7.8) and 19.2 (SD=10.2), respectively. At 1-month, mean self-reported (CPTS-RI) and interviewer-based (CAPS-CA) PTSD symptom scores were 23.2 (SD=12.1) and 19 (SD=16.9), respectively. According to the CAPS-CA, 5 children (4.9%) suffered from full PTSD. Bivariate analyses demonstrated a significant association between peritraumatic variables (PDI and PDEQ) and both CAPS-CA and CPTS-RI (r=0.22-0.57; all p< 0.05). However, in a multivariate analysis, PDI was the only significant predictor of acute PTSD symptoms (Beta=0.33, p< 0.05). Conclusion As has been found in adults, peritraumatic distress is a robust predictor of who will develop PTSD symptoms among school-aged children.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".