Physical fitness level and the risk of severe COVID-19: A systematic review
Bibliographic record
Abstract
To verify systematically the association between the status of physical fitness and the risk of severe Coronavirus disease 2019 (COVID-19). This systematic review is in accordance with the Preferred Reporting Items for Systematic Review and Meta Analyses (PRISMA) statement and the eligibility criteria followed the Population, Intervention, Comparison, Outcomes and Study (PICOS) recommendation. PubMed, Embase, SciELO and Cochrane electronic databases were searched. All studies that explored the relationship between the pattern of physical fitness and COVID-19 adverse outcomes (hospitalization, intensive care unit admission, intubation, or mortality), were selected. The quality of the studies was assessed by the specific scale of the Newcastle-Ottawa Scale. A total of seven observational studies were identified in this systematic review; 13 468 patients were included in one case-control study, two cohort studies, and four cross-sectional studies. All studies reported an inverse association between high physical fitness and severe COVID-19 (hospitalization, intensive care admission, or mortality). Only some studies reported comorbidities, especially obesity and cardiovascular disorders, but the results remained unchanged after controlling for comorbidities. The quality of the seven studies included was moderate according to the Newcastle-Ottawa Quality Assessment Scale. The methodological heterogeneity of the studies included did not allow a meta-analysis of the findings. In conclusion, higher physical fitness levels were associated with lower risk of hospitalization, intensive care admissions, and mortality rates among patients with COVID-19.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".