Exercise Capacity and Fatigue in Post-COVID-19 Patients
Bibliographic record
Abstract
Background: While many researchers have investigated the influence of COVID-19 on fatigue and quality of life, its impact on exercise capacity has been little considered. It is therefore our aim to examine the impact of COVID-19 on exercise capacity and fatigue among individuals who have recovered from the virus. Methods: A cross-sectional study was conducted at the outpatient physical therapy department of a tertiary hospital and Primary health care center. The study comprised 116 participants divided into two groups: a normative group composed of individuals who had not been infected with COVID-19 in the past three months, and a control group consisting of those who had contracted COVID-19 within the preceding three months. The one-minute sit-to-stand test (1STST) was carried out to assess exercise capacity, following which fatigue was measured using the validated Arabic version of the Fatigue Severity Scale (FSS). Results: Of the 116 participants enrolled in this study, 76 (65.5%) were in the normative group and 40 (34.5%) in the control group. Following the intervention, the mean FSS score differed significantly between the normative (26.6; SD 10.9) and the control group (36.9; SD 14.8); p-value < 0.001, with participants in the control group reporting higher levels of weariness than those in the normative group. Moreover, as measured by 1STST, the median number of sit-to-stand repetitions completed by participants in the normative group (21.0) was considerably greater than that of the control group (20.0); p-value = 0.025. Conclusion: Participants in the control group reported higher levels of fatigue and demonstrated lower exercise capacity than those in the normative group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".