Bibliographic record
Abstract
The increasing survival rate after discharge from the intensive care unit (ICU) has revealed long-term impairments in the cognitive, psychiatric, and physical domains among survivors. However, clinicians often fail to recognize this post-ICU syndrome (PICS) and its debilitating effects on family members (PICS-F). This study describes two cases of PICS to illustrate the different impairments that may occur in ICU survivors. The PICS risk factors for each domain and the interactions among risk factors are also described. In terms of diagnostic evaluation, limited evidence-based or validated tools are available to assist with screening for PICS. Clinicians should be aware to monitor for its symptoms on the basis of cognitive, psychiatric, and physical domains. The Montreal Cognitive Assessment is recommended to screen for cognition, as it has a high sensitivity and can evaluate executive function. Mood disorders should also be screened. For mobile patients, a 6-minute walk test should be performed. PICS can be prevented by applying the ABCDEF bundle ABCDEF bundle in ICU described in this paper. Finally, the family members of patients in the ICU should be involved in patient care and a tactful communication approach is required to reduce the risk of PICS-F.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".