Headache as an acute and post‐COVID‐19 symptom in COVID‐19 survivors: A meta‐analysis of the current literature
Bibliographic record
Abstract
BACKGROUND: Headache is identified as a common post-COVID sequela experienced by COVID-19 survivors. The aim of this pooled analysis was to synthesize the prevalence of post-COVID headache in hospitalized and non-hospitalized patients recovering from SARS-CoV-2 infection. METHODS: MEDLINE, CINAHL, PubMed, EMBASE, and Web of Science databases, as well as medRxiv and bioRxiv preprint servers, were searched up to 31 May 2021. Studies or preprints providing data on post-COVID headache 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 post-COVID headache. Data synthesis was categorized at hospital admission/symptoms' onset, and at 30, 60, 90, and ≥180 days afterwards. RESULTS: From 9573 studies identified, 28 peer-reviewed studies and 7 preprints were included. The sample was 28,438 COVID-19 survivors (12,307 females; mean age: 46.6, SD: 17.45 years). The methodological quality was high in 45% of the studies. The overall prevalence of post-COVID headache was 47.1% (95% CI 35.8-58.6) at onset or hospital admission, 10.2% (95% CI 5.4-18.5) at 30 days, 16.5% (95% CI 5.6-39.7) at 60 days, 10.6% (95% CI 4.7-22.3) at 90 days, and 8.4% (95% CI 4.6-14.8) at ≥180 days after onset/hospital discharge. Headache as a symptom at the acute phase was more prevalent in non-hospitalized (57.97%) than in hospitalized (31.11%) patients. Time trend analysis showed a decreased prevalence from the acute symptoms' onset to all post-COVID follow-up periods which was maintained afterwards. CONCLUSION: This meta-analysis found that the prevalence of post-COVID headache ranged from 8% to 15% during the first 6 months after SARS-CoV-2 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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.051 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".