Assessment of symptoms of the post-COVID-19 syndrome in patients with different rheumatic diseases
Bibliographic record
Abstract
Abstract Background Patients with rheumatic diseases significantly suffer during and after infection with coronavirus disease (COVID-19). Post-COVID-19 syndrome (PCS) refers to signs and symptoms occurring during or following a COVID-19 infection that continue beyond 12 weeks. The study aimed to assess PCS symptoms in rheumatic disease patients compared to a control group not suffering from a rheumatic disease or any other chronic illness. Results The prevalence of PCS symptoms was significantly higher in rheumatic disease patients compared to the control group: fatigue (69.1% vs. 41.25%), myalgia (73.5% vs. 37.5%), attention deficits (57.4% vs. 40%), and muscle weakness (33.8% vs. 13.8%). Objectively, the study group had significantly higher scores for the Fatigue Severity Scale (FSS) (35.46 ± 13.146 vs. 25.1 ± 7.587), Short-form McGill Pain Questionnaire (SF-MPQ-2) (21.66 ± 10.3 vs. 11.6 ± 3.433), and higher grades of functional disability in the Post-COVID-19 Functional Status scale (PCFS). Rheumatic disease patients had significantly higher frequencies of anxiety and depression, as assessed by the Hospital Anxiety and Depression Scale (HADS), and cognitive impairment, as assessed by the Mini-Mental State Examination (MMSE), than the controls (P = 0.023,P = 0.003,P = 0.0001, respectively). Moreover, SLE patients had the most symptoms and the highest FSS, SF-MPQ-2, PCFS, and HADS scores, as well as the lowest MMSE scores (P = 0.0001 for all except cough (P = 0.043), weakness (P = 0.015), paresthesia (P = 0.027), and anosmia (P = 0.039)). Lower disease duration, hospitalization during acute COVID-19, steroid use, smoking, and biologics non-use were significantly associated with higher PCS symptoms. Smoking was a significant risk factor (P = 0.048), and biologics use was protective (P = 0.03). Rheumatic disease patients who received two doses of the COVID-19 vaccinations had better scores on the FSS, HADS for anxiety and depression, and MMSE than those who received a single dose (P = 0.005,P = 0.001,P = 0.009,P = 0.01). Conclusion Rheumatic disease patients have a higher prevalence and risk of PCS, so strict follow-up, avoiding smoking, controlling disease activity, and COVID-19 vaccinations are essential for decreasing the morbidity of PCS.
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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) | 0.001 | 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) | 0.002 | 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".