Frequency and spectrum of Post-Intensive Care Syndrome (PICS) in survivors of critical illness in a tertiary care hospital in Pakistan.
Bibliographic record
Abstract
Objective: To determine the frequency and spectrum of post-intensive care syndrome (PICS) in survivors of critical illness at a tertiary care hospital of Pakistan. Study Design: Cross-sectional study. Setting: Intensive Care Unit (ICU) of the Sindh Institute of Urology and Transplantation, Karachi, Pakistan. Period: November 2022 to July 2023. Methods: Critical illness survivors aged 18-75 years and discharged from intensive care units ICUs were analyzed. PICS was defined as new or worsening problem(s) in physical, cognitive, or mental health status arising after critical illness and persisting beyond acute care hospitalization. Montreal Cognitive Assessment (MoCA), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Clinical Frailty Scale (CFS) were used to evaluate cognitive, psychiatric, and physical impairments. Results: There were 91 patients, with a median age of 40 years (IQR = 23 - 48). Physical impairment was observed in 49.4% of patients, whereas cognitive impairment was found in 67%, and psychiatric impairment (based on PHQ-9 scores of 5 or greater and GAD-7 scores of 5 or greater) was observed in 49.4% and 45.0% of patients, respectively. Overall, the frequency of PICS was found to be 84.6% in our study, and the frequency of patients with impairment in 1, 2, or 3 domains was 33%, 14.3%, and 37.4%, respectively. Conclusion: PICS is a highly prevalent syndrome in survivors of critical illness. Shock is a statistically significant risk factor for PICS. Cognitive impairment appears to be the most common domain of PICS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".