Poor long-term recovery after critical COVID-19 during 12 months longitudinal follow-up
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe the burden of illness and impact on health and working situation among former intensive care patients treated for COVID-19. METHODS: A prospective cohort study was performed at one intensive care unit of a university hospital in Sweden during the first wave of COVID-19 in spring 2020. The burden of illness in health status, cognitive, physical, and psychological outcomes, and working situation were assessed at four and 12 months after discharge from intensive care, using nine validated instruments. RESULTS: Forty-six participants treated for COVID-19 participated in both follow-ups and were included in this study. General fatigue was reported by 37 of 46 participants (82%) at both follow-ups (p = 1.000). For overall health status 28 (61%) participants at the first follow-up and 26 (57%) (p = 0.414) at the second reported lower values than the general population. Cognitive impairment was seen in 22 (52%) participants at four months and in 13 (31%) at 12 months (p = 0.029). The proportion of participants on sick-leave decreased between the first and second follow-up (24% vs 13%, p = 0.025), but the proportion of participants working full-time was almost the same at both follow-ups (35% vs 37%, p = 0.317). CONCLUSIONS: The burden of illness of patients treated in intensive care due to COVID-19 included cognitive, physical, and psychological impacts. Cognitive functions were improved after 12 months, but no clear improvements could be distinguished in the physical or psychological outcome. Higher burden of illness was associated with inability to return to work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".