Association between sex and mental health sequelae after ICU discharge: A scoping review
Bibliographic record
Abstract
Background: Following discharge from intensive care units (ICUs), more than 50% of patients may develop mental health conditions including depression, post-traumatic stress disorder (PTSD), and anxiety. However, there is limited research to suggest risk factors and new possibilities for management. Objective: Are there sex-related differences in the incidence, severity, and duration of mental health sequelae in adults after ICU discharge? Methods: Studies published in English within the last 20 years, focusing on sex and mental health sequelae post-ICU. Online databases MedLine, EmBASE, Scopus, PsycINFO, and CINAHL were explored as of September 22, 2021. Results: Of the 706 studies screened, six were included, and three demonstrated a statistically significant association between sex and mental health sequelae. Four outcomes of interest were noted: mental health-related quality of life (HRQoL), post-traumatic stress symptoms (PTSS), major depression/PTSD comorbidity, and major depression. Three studies that addressed mental HRQoL noted a decreased mental HRQoL in females compared to males, but two were statistically significant. No statistically significant association was found between sex and PTSS. One study examined both major depression/PTSD comorbidity and major depression and found a statistically significant association between female sex and both outcomes. Conclusions: Despite methodological limitations of the identified studies, this scoping review shows a trend for worse mental health outcomes in females post-ICU. More research focusing on confounding factors is needed to better understand the associations between sex, gender, and mental health sequelae in post-ICU patients.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".