The importance of physical activity in the elderly population with comorbidities in a post-pandemic era
Bibliographic record
Abstract
During the COVID-19 pandemic, the world faced significant challenges that impacted all age groups. Among the most vulnerable groups, the elderly population with comorbidities had the greatest challenge to their physical, social, andmental health. The lockdown imposed by health authorities aimed at slowing the spread of the virus had a large drawback in terms of the level of physical activity, risk factors, frailty, and falls risk in the elderly. Furthermore, several health issues were exacerbated, including higher levels of obesity, diabetes mellitus, dyslipidemia, cardiovascular diseases, sleep problems, and depression. A decrease in the level of physical activity was observed following quarantine, and this trend prevailed even a year after the early stages of the pandemic. A large populational study in individuals more than 65 years old showed that during the early years of the pandemic, nearly 30% of them experienced an impairment in exercise levels, which was significantly related to detriments in quality of life, and this trend prevailed even a year after the early stages of the pandemic. Given this post-pandemic scenario, it is essential that we redefine strategies to improve the quality of life of these demographics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".