Headache as an acute and post‐COVID‐19 symptom in COVID‐19 survivors: A meta‐analysis of the current literature
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
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 it