COVID-19 survivors with Post Intensive Care Syndrome: Key rehabilitation strategies for Africa
Bibliographic record
Abstract
The emergence of coronavirus diseases (COVID-19) has presented a global health threat. The number of COVID-19 cases continues to increase in Africa; this poses a challenge to the African healthcare system, particularly the intensive care unit (ICU). More so, individuals with severe COVID-19 would often have a prolonged intensive care stay, requiring mechanical ventilation and sedation and this may increase their risk of developing physical, cognitive and psychosocial impairments. These post-ICU sequelae of morbidities have been termed Post Intensive Care Syndrome. The African healthcare system needs to be prepared to manage the adverse effects of Post Intensive Care Syndrome (PICS) largely characterized by a decline in functional capacity and health-related quality of life. It is thus expedient that multi-targeted measures such as early rehabilitation, adequate screening, patient/caregiver education and post-discharge rehabilitation be adopted to combat imminent poor health outcomes in COVID-19 survivors. In this editorial, we provided a brief review of PICS and highlighted strategies for preventing and managing PICS in the critically ill within the African context Key words: Pandemic, COVID-19 Survivors, African healthcare, Intensive Care Unit, Rehabilitation
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".