Misperception of Visual Verticality in Patients with Primary Headache Disorders: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Migraine and tension-type headache (TTH) are the two most prevalent primary headache disorders (PHDs) that may involve visual and vestibular impairments, neck pain, and postural unsteadiness. The perception of visual verticality (VV) has been studied in patients diagnosed with PHD to assess balance disorders showing varying findings. Our study aimed to assess the VV perception in patients diagnosed with PHD in comparison to healthy controls. A systematic review with meta-analysis was carried out in PubMed MEDLINE, Scopus, WOS, CINAHL, and SciELO. The Cohen standardized mean difference (SMD) was used to estimate the differences between exposed and healthy controls. Seven studies with 816 participants were included. The quality of included studies, according to the Newcastle–Ottawa Scale (NOS), was moderate (mean score of 5.2). Patients diagnosed with PHD showed a moderate misperception of VV as assessed with the subjective visual vertical (SVV) test (SMD = 0.530; 95% CI = 0.225, 0.836; p < 0.001). Specifically, a misperception of the SVV was found in patients with migraine (SMD = 0.369; 95% CI = 0.1, 0.638; p = 0.007) and with TTH (SMD = 1.122; 95% CI = 0.540, 1.704; p < 0.001). This review shows a misperception of VV in patients with migraine and TTH when assessed with the SVV test, being higher in patients with TTH, although the THH sample size was low.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".