The role of in-hospital traumatic distress after out-of-hospital cardiac arrest in later fatigue, sleep quality, and health-related quality of life
Bibliographic record
Abstract
AIM: To investigate potential in-hospital cognitive and psychopathological factors associated with fatigue three months after out-of-hospital cardiac arrest (OHCA). METHODS: This was a multicenter prospective cohort study conducted across three heart centers in Denmark. While in-hospital, OHCA survivors were screened for cognitive impairment using the Montreal Cognitive Assessment, self-reported symptoms of anxiety and depression using the Hospital Anxiety and Depression Scale, and traumatic distress using the Impact of Event Scale - Revised. At three-month follow-up, fatigue severity was assessed with the Fatigue Severity Scale (FSS). FSS ≥ 4 indicates clinically important fatigue. Logistic regression models were applied. RESULTS: Overall, 173 survivors were included (mean age 63.1 ± 11.7 years). At follow-up, the median FSS score was 3.2 points (IQR 2-9) and 42 % of survivors presented with clinically important fatigue (FSS ≥ 4). Those with fatigue were more often female, had longer hospital stays, reported greater in-hospital symptoms of anxiety, depression and traumatic distress, poorer sleep quality and health-related quality of life at follow-up. In the multivariable regression model, including age, sex, length of stay, anxiety, depression, traumatic distress and sleep quality, only traumatic distress was independently associated with FSS ≥ 4 (OR 4.6, 95 % CI: 1.5-14.7, p = 0.009). CONCLUSION: More than a third of OHCA survivors self-reported fatigue at three-month follow-up. In-hospital symptoms of traumatic distress were associated with higher odds of fatigue. While these findings underscore the potential value of early identification of traumatic distress, further research is needed to evaluate the benefits of screening and to identify interventions to support recovery after cardiac arrest.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".