Prediction of the occurrence and intensity of post-traumatic stress disorder in victims 32 months after bomb attack
Bibliographic record
Abstract
INTRODUCTION: Our objective was to identify factors that predict occurrence and severity of post-traumatic stress disorder (PTSD) after a terrorism attack. POPULATION: We evaluated 32 victims of a bomb attack in a Paris subway in December 1996 at 6 and 32 months. METHOD: Sociodemographic characteristics, clinical data and physical injuries were used to predict PTSD occurrence and severity in 32 victims. The Watson's PTSD Inventory (PTSD-I) and the Impact of Event Scale (IES) by Horowitz were used to evaluate occurrence and severity of PTSD, respectively. RESULTS: Thirty-nine percent of participants met PTSD criteria at 6 months, 25% still had PTSD at 32 months. Women had PTSD 32 months after the bomb attack more frequently than men. Employment predicted PTSD severity at 32 months. PTSD scores assessed by PTSD-I at 6 months were significantly and positively associated with IES scores at 32-month follow-up (r = 0.55, P = 0.004). Psychotropic drug use before the bomb attack significantly predicted PTSD occurrence and severity at 6 and 32 months. In a linear regression model, physical injuries, employment status and psychotropic drug use before the bomb attack were independent predictors of severity of PTSD at 32 months. CONCLUSIONS: Bomb attack exposure resulted in persisting PTSD in a significant proportion of victims; the severity was predicted at 32 months by physical injuries and psychotropic drug use before the terrorism attack and by the PTSD score few months after the bomb attack.
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) | 0.002 | 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".