Prevalence of Headache in Patients With Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis of 14,275 Patients
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) started to spread globally since December 2019 from Wuhan, China. Headache has been observed as one of the clinical manifestations in COVID-19 patients. We aimed to conduct a comprehensive systematic review and meta-analysis to estimate the overall pooled prevalence of headache in COVID-19 patients. Methods: PubMed, Scopus, ScienceDirect, and Google Scholar databases were searched to identify studies published between December 2019 and March 2020. Adult (≥18 years) COVID-19 patients were considered eligible. We used random-effects model to estimate the pooled prevalence with 95% confidence intervals (CIs). Quality assessment was done using the Joanna Briggs Institute critical appraisal tools. This study is registered with PROSPERO (CRD42020182529). Results: We identified 2,055 studies, of which 86 studies ( n = 14,275, 49.4% female) were included in the meta-analysis. Overall, the pooled prevalence of headache in COVID-19 patients was 10.1% [95% CI: 8.76–11.49]. There was no significant difference of headache prevalence in severe or critical vs. non-severe (RR: 1.05, p = 0.78), survived (recovered or discharged) vs. non-survived (RR: 1.36, p = 0.23), and ICU vs. non-ICU (RR: 1.06, p = 0.87) COVID-19 patients. We detected 64.0, 34.9, and 1.1% of the included studies as high, moderate, and low quality, respectively. Conclusions: From the first 4-month data of the outbreak, headache was detected in 10.1% of the adult COVID-19 patients.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".