Global prevalence and pathogenesis of headache in COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
Background : This study was conducted to determine the prevalence of headache in coronavirus disease 2019 (COVID-19) and to assess its association as a predictor for COVID-19. This study also aimed to discuss the possible pathogenesis of headache in COVID-19. Methods : Available articles from PubMed, Scopus, and Web of Science were searched as of September 2 nd , 2020. Data on characteristics of the study, headache and COVID-19 were extracted following the PRISMA guidelines. Biases were assessed using the Newcastle-Ottawa scale. The cumulative prevalence of headache was calculated for the general population (i.e. adults and children). The pooled odd ratio (OR) with 95% confidence intervals (95%CI) was calculated using the Z test to assess the association between headache and the presence of COVID-19 cases. Results : We included 104,751 COVID-19 cases from 78 eligible studies to calculate the global prevalence of headache in COVID-19 and 17 studies were included to calculate the association of headache and COVID-19. The cumulative prevalence of headache in COVID-19 was 25.2% (26,464 out of 104,751 cases). Headache was found to be more prevalent, approximately by two-fold, in COVID-19 patients than in non-COVID-19 patients (other respiratory viral infections), OR: 1.73; 95% CI: 1.94, 2.5 with p=0.04. Conclusion : Headache is common among COVID-19 patients and seems to be more common in COVID-19 patients compared to those with the non-COVID-19 viral infection. No definitive mechanisms on how headache emerges in COVID-19 patients but several possible hypotheses have been proposed. However, extensive studies are warranted to elucidate the mechanisms. PROSPERO registration : CRD42020210332 (28/09/2020)
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".