An Exploratory Study to Determine the Relationship between Cervical Dysfunction and Perimenstrual Migraines
Bibliographic record
Abstract
PURPOSE: To determine whether an association between cervical dysfunction and perimenstrual migraines exists. METHODS: Forty perimenstrual migraine sufferers and 46 controls were compared. Information on the participants' ages and perceptions of neck pain and stiffness were solicited. The blinded physical examination of the cervical area consisted of postural, range of motion, muscle strength, muscle length, trigger point, neural mobility, and segmental cervical joint movement assessment. RESULTS: The migraine group had increased perception of neck pain and stiffness (p<0.001); reduced bilateral rotation (p=0.013); decreased muscle length in both trapezii, left sternocleidomastoid, and right occipitals (p=0.045); more pain on muscle stretch in both levator scapulae, both trapezii, left sternocleidomastoid, and both occipitals (p=0.013); increased trigger points bilaterally in the left trapezius (p=0.021), right trapezius (p=0.023), left sternocleidomastoid (p=0.0.004), and right sternocleidomastoid (p=0.021); reduced neural mobility with bilateral elbow lag (p=0.043); greater C4-C6 pain (p=0.045); and increased cervical stiffness in C5-C7 (p=0.023). There were no differences in posture and muscle strength. Decreased muscle length increased the risk of perimenstrual migraines 2.4-6.7 fold, reduced neural mobility 5.8-10.7 fold, and increased C7 stiffness 17.0 fold. CONCLUSION: The results suggest that an association between cervical dysfunction and perimenstrual headaches should be further explored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".