Post-intensive care syndrome in critically-ill COVID-19 survivors followed for one-year
Bibliographic record
Abstract
ObjectiveTo document the occurrence of post-intensive care syndrome (PICS) in intensive care unit (ICU) survivors with coronavirus disease-2019 (COVID-19) up to one year.MethodsRetrospective observational study at a university hospital post-ICU outpatient clinic. Patients were followed up in-person at 1 month, 3 months, 6 months and one-year after hospital discharge. Cognitive, physical and psychological domains of PICS were evaluated. PICS was defined as at least one dysfunction in the assessment tools in each domain.ResultsSixty-four patients were evaluated during the study period. Median age was 62.5 (55.0-71.0). Fifty-eight percent of them were male. Median APACHE II and admission SOFA scores were 13 (10-16) and 3 (3-4), respectively. Sixty-four, 54, 44, 20 patients were evaluated during the 1 -month, 3-month, 6-month and one-year visits. 94% of patients had PICS at the 1st visit and this declined to 75% in one-year. The ratio of patients who fulfilled all PICS domains were 15%, 10%, 13% and 13%, respectively at 4 follow-up visits. Physical impairment was the most commonly observed dysfunction during all visits.DiscussionThis study showed that at least one domain of PICS persisted in 75% of patients at one-year in COVID-19 ICU survivors.
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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 | 0.000 |
| 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) | 0.001 | 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".