Time course prevalence of post-COVID pain symptoms of musculoskeletal origin in patients who had survived severe acute respiratory syndrome coronavirus 2 infection: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT: The aim of this review or meta-analysis is to synthesize the prevalence of post-coronavirus disease (COVID) pain symptoms of musculoskeletal origin in hospitalized or nonhospitalized patients recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MEDLINE, CINAHL, PubMed, EMBASE, and Web of Science databases, as well as medRxiv and bioRxiv preprint servers were searched up to May 1, 2021. Studies or preprints reporting data on post-COVID pain symptoms such as myalgias, arthralgias, or chest pain after SARS-CoV-2 infection and collected by personal, telephonic, or electronical interview were included. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale. Random-effects models were used for meta-analytical pooled prevalence of each post-COVID musculoskeletal pain symptom. Data synthesis was categorized at onset or hospital admission and at 30, 60, and 90, and ≥180 days after. From a total of 12,123 studies identified, 27 peer-reviewed studies and 6 preprints were included. The sample included 14,639 hospitalized and 11,070 nonhospitalized COVID-19 patients. The methodological quality of almost 70% studies was fair. The overall prevalence of post-COVID myalgia, joint pain, and chest pain ranged from 5.65% to 18.15%, 4.6% to 12.1%, and 7.8% to 23.6%, respectively, at different follow-up periods during the first year postinfection. Time trend analysis showed a decrease prevalence of musculoskeletal post-COVID pain from the symptom's onset to 30 days after, an increase 60 days after, but with a second decrease ≥180 days after. This meta-analysis has shown that almost 10% of individuals infected by SARS-CoV-2 will suffer from musculoskeletal post-COVID pain symptomatology at some time during the first year after the infection.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".