Assessment of pain, physical activity, and overall health status in individuals who have had COVID-19
Bibliographic record
Abstract
Aim: COVID-19 is a viral disease that first emerged in Wuhan, a city in China's Hubei province, and has been declared a pandemic. The aim of this study is to evaluate pain, physical activity, and general health status in individuals who have had COVID-19. Methods: The study was conducted on 58 men and 76 women who had recovered from COVID-19. The Physical Activity Questionnaire, McGill-Melzack Pain Questionnaire, and Nottingham Health Profile (NHP) Questionnaire were administered to assess the participants' pain, physical activity, and overall health status. The Anderson-Darling test was applied for normality analysis, the Two-Simple T test was applied to examine the relationship between normally distributed parameters, and the Mann-Whitney U test was applied for non-normally distributed parameters. To determine the relationship and degree between parameters, the Pearson Correlation test was applied to normally distributed parameters and the Spearman rho test was applied to non-normally distributed parameters. Results: At the end of the study, it was found that after Covid-19, male individuals most frequently described “leaking” pain followed by “throbbing” pain, while female individuals described ‘throbbing’ pain followed by “leaking” pain. For protection, 72.41% of male individuals and 72.37% of female individuals were found to have received the Biontech vaccine. A significant difference was found between genders in terms of physical activity (p<0.05). When comparing the surveys according to physical activity status, a significant difference was found in McGill-Melzack scores between the very active-minimal active and very active-inactive groups (p<0.05). No significant difference was found between any groups for NHP 1 and NHP 2 scores (p>0.05). Conclusion: The study evaluated pain, physical activity, and overall health status in individuals who had contracted COVID-19. We believe it will make significant contributions to clinical sciences in this regard.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".