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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it