Long <scp>COVID</scp> in Persons With <scp>Self‐Reported</scp> Arthritis: Symptoms, Associated Factors, and Functional Limitations
Bibliographic record
Abstract
OBJECTIVE: The aim is to describe both long COVID symptoms and associated factors in a cohort of individuals with a self-reported history of arthritis as well as change in function in persons with arthritis and long COVID compared to pre-COVID status. METHODS: Among 2,764 persons with a confirmed COVID-19 diagnosis who responded to an online survey at least 12 weeks post-infection, 171 reported a history of arthritis and formed our study sample. We calculated the frequency of long COVID defined as troubled by persistent symptoms and evaluated associated factors using bivariate analysis and multivariable logistic regression. Among those with long COVID, we describe limitations in activity and function in comparison to pre-COVID status. RESULTS: In our sample, 53.5% (n = 91) reported being troubled by ongoing symptoms at the time of completing the questionnaire (long COVID), with the most frequent symptoms as the following: fatigue, myalgia, weakness, breathlessness, low mood, anxiety, and sleep disturbance. Factors associated with long COVID were female sex, having been hospitalized for COVID, and having at least 1 other chronic disease. Persons with long COVID had substantial declines in function, notably in global health status, usual activities, mobility, personal care, and employment status. Also, 37% of those with long COVID reported moderate to severe increase in pain. CONCLUSION: Persons with arthritis who have long COVID have substantial limitations in function compared to their pre-COVID status. There is a need to implement effective interventions to improve functional status in persons with arthritis and long COVID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".