Pain and Clinical Presentation: A Cross-Sectional Study of Patients with New-Onset Chronic Pain in Long-COVID-19 Syndrome
Bibliographic record
Abstract
The aim of this study was to evaluate the characteristics of pain (i.e., pain intensity, pain interference, clinical presentation) in Long-COVID-19 patients and compare the location of pain between successfully recovered COVID-19 patients and healthy matched controls. A cross-sectional case-control study was carried out. Long-COVID-19 patients, age- and sex-matched patients with a history of COVID-19 who had successfully recovered, and healthy controls were included. Outcomes included were pain characteristics (Brief Pain Inventory and Short-Form McGill Pain Questionnaire) and clinical presentation (Widespread Pain Index and Euroqol-5 Dimensions 5 Levels Visual Analogue Scale). Sixty-nine patients with Long-COVID-19 syndrome, sixty-six successfully recovered COVID-19 patients, and sixty-seven healthy controls were evaluated. Patients with Long-COVID-19 syndrome showed greater pain intensity and interference. In addition, they showed worse quality of life and greater widespread pain, with the most frequent locations of pain being the neck, legs, and head. In conclusion, patients with Long-COVID-19 syndrome show a high prevalence of pain, characterized by widespread pain of moderate intensity and interference, with the most frequent locations being the neck, legs, and head, significantly affecting the quality of life of these patients.
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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 | 0.002 |
| 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 | 0.001 |
| 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".