P.038 The incidence and characteristics of chronic pain and fatigue after 12 months later admitting with COVID-19; The Post- COVID 19 syndrome
Bibliographic record
Abstract
Background: This study aimed to evaluate chronic pain and fatigue in COVID-19 patients after 12 months of hospitalization. Methods: We studied the COVID-19 patients discharged from Hospital, between March 10 to April 20, 2020. Results: A total of 157 patients were included in this study. Forty-three patients (27.4%) complained of chronic fatigue and muscle weakness in the last six months. The visual analog fatigue scale (VAFS) score of 3.84 ± 1.48 was obtained. Forty patients (25.5%) were suspected of Chronic Fatigue Syndrome (CFS). Twenty-four patients (15.3%) had severe chronic pain or exacerbation of previous chronic pain, most of which were reported in the lower back (70.8%) and lower extremities (66.7%). Pain intensity had a mean score of 2.33 ± 0.87 and was mainly described as “muscle cramps,” “persistent dull pain,” and “boring and numbing.” In women, chronic pain and fatigue, extended hospital stays, ICU admission, and depressed mood were common than in men. For these pain and fatigue, 37% used nonsteroidal anti-inflammatory drugs, and 16.3% used antidepressants. Only one person had applied for physiotherapy, and none of the patients had received psychotherapy Conclusions: Fatigue and chronic pain in patients recovering from COVID-19 are common complications, even after 12 months of illness.
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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 | 0.000 | 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) | 0.006 | 0.001 |
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".