Hallmarks of primary headache: part 2– Tension-type headache
Bibliographic record
Abstract
BACKGROUND AND AIM: Tension-type headache is the most prevalent primary headache disorder. While the episodic subtype is more common, chronic tension-type headache significantly impacts health-related quality of life and contribute to increased healthcare utilization and disability. Despite considerable advances in the understanding of tension-type headache, critical gaps persist. This paper aims to provide a comprehensive review of the hallmarks of tension-type headache, from its pathophysiology, comorbidities, treatment options, to psychosocial impact. MAIN RESULTS: Multiple factors are associated with tension-type headache, including peripheral mechanisms (increased muscle tenderness and myofascial trigger points), central sensitization, genetic predisposition, and psychological comorbidities such as anxiety and depression. Neuroimaging and neurophysiological studies demonstrated altered pain processing in cortical and subcortical regions in patients with tension-type headache. Regarding treatment strategy, in addition to pharmacological treatment, novel insights into non-pharmacological interventions such as cognitive behavioral therapy, neuromodulation techniques, physical therapy, mindfulness, lifestyle management, and patient education were highlighted as valuable components of comprehensive management strategies. CONCLUSIONS: A complex interplay between peripheral and central mechanisms and psychosocial stressors underpins tension-type headache. Integrated multidisciplinary approaches combining pharmacological and non-pharmacological interventions are critical for optimal patient outcomes. Further research should continue to refine the understanding of these mechanisms to improve targeted therapeutic strategies and reduce the global burden of tension-type headache.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".