Symptoms of Acute Posttraumatic Stress Disorder After Intensive Care
Bibliographic record
Abstract
BACKGROUND: Admission to intensive care is often a sudden and unexpected event precipitated by a life-threatening condition, 2 determinants thought to influence the development of posttraumatic stress disorder. OBJECTIVES: To identify the frequency of acute symptoms of posttraumatic stress disorder and to describe factors predictive of these symptoms in patients 1 month after discharge from intensive care. METHODS: In this prospective cohort study, all patients meeting the inclusion criteria during the study period were invited to participate. Participants completed the Impact of Event Scale-Revised, and demographic and clinical data were accessed from an intensive care unit database. RESULTS: During a 9-month period, 114 of 137 patients who met the inclusion criteria consented to participate in the study, and 100 (88%) completed it. The mean total score on the Impact of Event Scale-Revised was 17.8 (SD, 13.4; possible range, 0-88). A total of 13 participants (13%) scored higher than the cutoff score for clinical posttraumatic stress disorder. Neither sex nor length of stay was predictive of acute symptoms of post-traumatic stress disorder. In multivariate analysis, the only independent predictor of symptoms was age. Patients younger than 65 years were 5.6 times (95% confidence interval, 1.17-26.89) more likely than those 65 years and older to report symptoms. CONCLUSION: The rate of symptoms of posttraumatic stress disorder 1 month after discharge from intensive care was relatively low. Consistent with findings of previous research, being younger than 65 years was the only independent predictor of symptoms.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".