A nationwide US study of post-traumatic stress after hospitalization for physical injury
Bibliographic record
Abstract
BACKGROUND: Injured survivors of individual and mass trauma are at risk for developing post-traumatic stress disorder (PTSD). Few investigations have assessed PTSD after injury in large samples across diverse acute care hospital settings. METHOD: A total of 2931 injured trauma survivors aged 18-84 who were representative of 9983 in-patients were recruited from 69 hospitals across the USA. In-patient medical records were abstracted, and hospitalized patients were interviewed at 3 and 12 months after injury. Symptoms consistent with a DSM-IV diagnosis of PTSD were assessed with the PTSD Checklist (PCL) 12 months after injury. RESULTS: Approximately 23% of injury survivors had symptoms consistent with a diagnosis of PTSD 12 months after their hospitalization. Greater levels of early post-injury emotional distress and physical pain were associated with an increased risk of symptoms consistent with a PTSD diagnosis. Pre-injury, intensive care unit (ICU) admission [relative risk (RR) 1.17, 95% confidence interval (CI) 1.02-1.34], pre-injury depression (RR 1.33, 95% CI 1.15-1.54), benzodiazepine prescription (RR 1.46, 95% CI 1.17-1.84) and intentional injury (RR 1.32, 95% CI 1.04-1.67) were independently associated with an increased risk of symptoms consistent with a PTSD diagnosis. White injury survivors without insurance demonstrated approximately twice the rate of symptoms consistent with a diagnosis of PTSD when compared to white individuals with private insurance. By contrast, for Hispanic injury survivors PTSD rates were approximately equal between uninsured and privately insured individuals. CONCLUSIONS: Nationwide in the USA, more than 20% of injured trauma survivors have symptoms consistent with a diagnosis of PTSD 12 months after acute care in-patient hospitalization. Coordinated investigative and policy efforts could target mandates for high-quality PTSD screening and intervention in acute care medical settings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".