Trajectories of post-discharge cognitive function and associated risk factors in ICU survivors: a secondary analysis
Bibliographic record
Abstract
Cognitive impairment is a significant concern among intensive care unit (ICU) survivors, yet post-discharge cognitive trajectories remain understudied. This study aimed to explore cognitive function trajectories and associated factors in ICU survivors after hospital discharge. This secondary analysis used data from a prospective post-ICU cohort established in Busan, South Korea. A total of 329 ICU survivors who completed cognitive assessments at both early and late follow-up were included. Cognitive function was measured using the Montreal Cognitive Assessment. Approximately 40% of ICU survivors experienced mild cognitive impairment (MCI) within 12 months. Participants were classified into four groups based on the trajectory of cognitive impairment status: Normal, Recovered, Delayed Onset, and Persistent. Compared to the Normal Group, older age and comorbidities were significantly associated with the Recovered MCI Group. In the Delayed Onset MCI Group, older age, admission through emergency department, and trauma-related admission were determinants. In the Persistent MCI Group, factors included older age, lower education level, comorbidities, and discharge to an extended care facility. Cognitive trajectories may vary among ICU survivors after discharge. Based on our findings, a better understanding of post-discharge cognitive trajectories may help guide long-term monitoring and individualized rehabilitation strategies to improve outcomes in this vulnerable population.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".