Late Onset and Persistence of Post‐Traumatic Stress Disorder Symptoms in Survivors of Critical Care
Bibliographic record
Abstract
BACKGROUND: Several recent studies have reported that post-traumatic stress disorder (PTSD) is a frequent occurrence in survivors of an intensive care unit (ICU) admission. OBJECTIVE: To assess the frequency of PTSD symptoms at three and nine months post-ICU admission and examine possible risk factors that predispose to the development of PTSD symptoms. METHOD: Using the following scales: Davidson Trauma Scale, Impact of Event Scale and the Post-traumatic Symptom Scale, 69 ICU survivors were assessed for PTSD symptoms at three months post-ICU admission. Of the original 69 patients, 37 completed the same questionnaires at the second follow-up at nine months post-ICU admission. Mean symptom levels for avoidance, intrusive thoughts and hyperarousal were calculated, and risk factors for the development of PTSD symptomatology were examined. RESULTS: Depending on which scale was used, 16% to 33% of ICU survivors met the criteria for PTSD at either three or nine months. Younger age and the use of a prescription psychoactive medication at time of ICU admission were both independently associated with a higher risk of developing PTSD symptoms. Interestingly, symptoms of hyperarousal worsened during the follow-up interval for female patients, while they remained constant for males. CONCLUSION: The frequency of PTSD symptoms was high in patients who survived an admission to the ICU. Depending on sex, symptoms may present and evolve differently. The adoption of screening tools and a multicentre ICU database in Canada is recommended to identify patients who are most at risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".