Physical impairments in individuals with Long COVID
Bibliographic record
Abstract
Objective The primary objective was to compare the physical capacities of individuals with Long COVID [Long COVID group (LCG)] to those who had COVID-19 but did not develop persistent symptoms [short COVID group (SCG)], and to individuals without a history of COVID-19 [control group (CG)]. The secondary objectives were to provide a comprehensive profile of sociodemographic and COVID-19 history of individuals with Long COVID, considering factors such as sex, gender, hospitalization, time since onset, and comorbidities, and b) identify self-reported and objective clinical measures explaining health-related quality of life (HRQoL) in individuals with Long COVID. Methods A total of 120 adults were included in each of the groups. Participants completed self-reported assessments covering HRQoL, comorbidities, pain, sleep, and fatigue. Physical assessments included handgrip strength (HGS), Short Physical Performance Battery (SPPB), 6-minute-walk-test (6MWT), perceived exertion during the 6MWT (Modified-Borg Scale), and daily step count during a 7-day period. Results Mean age (mean [SD]) for LCG, SCG, and CG was 44.2 [11.2], 42.1 [16.4], and 46[15.9], respectively. LCG showed significantly higher pain, comorbidities, and fatigue, along with lower HRQoL and sleep quality compared to the other groups. HGS, SPPB, and 6MWT performance were also significantly lower in LCG, while perceived exertion during 6MWT was higher. Finally, the number of steps per day was significantly lower in LCG. Higher prevalence of obesity and comorbidities were identified among those hospitalized after COVID-19. Fatigue, pain, comorbidities, and Step count were the variables explaining HRQoL for LCG ( R 2: 0.58; F = 35.9). Conclusion Long COVID individuals, on average 329 [146] days post-infection, experience enduring physical and health-related challenges, with significant implications for their overall well-being.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".