Trigger Point Sensitivity Is a Differentiating Factor between Cervicogenic and Non-Cervicogenic Headaches: A Cross-Sectional, Descriptive Study
Bibliographic record
Abstract
Purpose: A common factor in all forms of headache is the presence of myofascial trigger points (TrPs). The aim of this study was to compare the presentation of patients with cervicogenic headaches and patients with non-cervicogenic headaches in the sensitivity of TrPs in their upper trapezius, sternocleidomastoid, temporalis, and posterior cervical muscles. Method: This was a descriptive, cross-sectional study. The following variables were compared between patients with cervicogenic (n=20) and patients with non-cervicogenic (n=20) headaches: sensitivity (pain-pressure threshold) of TrPs in the upper trapezius, sternocleidomastoid, posterior cervical, and temporalis muscles (using a handheld, digital algometer); level of disability (using the Henry Ford Hospital Headache Disability Inventory questionnaire); demographics (age, sex); anthropometrics (BMI); and clinical presentation (duration and intensity of symptoms). The independent Student t-test and χ2 test were used to determine the differences between the two groups. Effect sizes (Cohen's d) were calculated when relevant. Results: The two groups were similar in level of disability, demographic and anthropometric data, and clinical presentation. However, TrP sensitivity in the right upper trapezius (p=0.006; Cohen's d=0.96) and the left upper trapezius (p=0.003; Cohen's d=1.06) muscles was higher in the cervicogenic group. Conclusions: Increased sensitivity of TrPs in the upper trapezius muscle may be used as a differentiating factor in the diagnosis of cervicogenic headaches. This finding emphasizes the importance of integrating this muscle into the rehabilitation programs of patients with cervicogenic 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".