The Effect of the PostureJac on Deep Cervical Flexor Endurance: Implications in the Management of Cervicogenic Headache and Mechanical Neck Pain
Bibliographic record
Abstract
The convergence of cervical and trigeminal afferents on second-order neurons in the trigeminocervical nucleus may refer pain from the upper cervical spine into the head and face. Furthermore, "bi-directional interactions" between trigeminal and upper cervical afferents may also explain neck symptoms of trigeminal origin (e.g., migraine). It is known that cervicogenic headache sufferers present with several musculoskeletal changes including poor endurance of the deep cervical flexor muscles. These intrinsic muscles of the neck contribute to stabilization and protection of the cervical spine and are critical for the control of both intervertebral motion and the cervical lordosis. The purpose of this study was to determine whether the use of the PostureJac (SomatoCentric Systems, Inc., Toronto, Ontario, Canada), a posture support and exercise jacket, was effective in enhancing deep cervical muscle endurance. Forty-five (45) female subjects, between the ages of 18 and 40 years, were randomly assigned to three groups consisting of the no-treatment control, the treatment-control (table stabilization), and the experimental (PostureJac) group. The outcome measure of deep cervical flexor muscle endurance was based on the Flexor Endurance Test and was recorded in seconds. The results indicated that the PostureJac group was superior to the no-treatment control (p=.001) and the treatment-control (p=.004) groups in terms of increasing endurance of the deep cervical flexors. Consequently, the PostureJac may be a useful therapeutic tool in the management of cervicogenic headache and mechanical neck pain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".