A Review of the Current State of Management and Post-Recovery Rehabilitation of COVID-19
Bibliographic record
Abstract
In all countries, COVID-19 has had a profound impact on society in general and on healthcare systems in particular. Its route of transmission is similar to that of SARS-CoV and MERS-CoV, and it spreads through respiratory droplets and direct physical contact. It displays greater infectivity rates than the two recent viral outbreaks (SARS and MERS), and early epide-miological studies reveal lower fatality rates. Elderly and multimorbid persons are at the highest risk of developing complications and death. Although men and women are thought to be at equal risk for COVID-19, more men than women are dying, presumably due to sex-based immunological differences and lifestyle factors such as smoking. N95 respirators are thought to be more effective in preventing viral transmission than surgical masks, but the evidence is limited and inconsistent. The current clinical management strategy of COVID-19 is the same as acute respiratory distress syndrome (ARDS). Novel treatments and therapies such as the use of plasmapheresis, chloroquine, hydroxychloroquine, and remdesivir have shown promising results in clinical trials for treating COVID-19. The roles of physical rehabilitation and medicine are expected to increase because postrecovery COVID-19 patients are experiencing cardiovascular, neurological, psychological, and cognitive sequalae. Innovative management strategies are needed to limit the spread of disease and to protect vulnerable persons. Significant evidence indicates that the use of telerehabilitation reduces hospitalizations and cardiac events compared to regular care. Actions are needed to prevent deconditioning in rehabilitation patients, and respiratory therapy practices will require close attention to reduce the dispersion of air droplets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".