Cervical Vertebral Realignment When Voluntarily Adopting a Protective Neck Posture
Bibliographic record
Abstract
STUDY DESIGN: In vivo human volunteer study of the intervertebral postural changes and muscle activity levels while tensing the neck muscles. OBJECTIVE: To determine if actively tensing the neck muscles changes the posture of the cervical spine and, because axial impact neck injury often occurs while inverted, whether these changes exist both upright and upside down. SUMMARY OF BACKGROUND DATA: Rollover accidents are dynamic and complex events in which head contacts with the vehicle interior can cause catastrophic neck injuries. Computational modeling has suggested that active neck muscles may increase the risk of cervical spine fracture in a rollover crash. Cadaver testing has also demonstrated that overall neck alignment and curvature are key to understanding and preventing catastrophic neck injuries. Although muscle activity and neck posture affects the resulting injury, there are currently no in vivo data describing how tensing the neck muscles influences intervertebral posture. METHODS: Eleven human subjects (6 females, 5 males) actively tensed their neck muscles while seated upright and inverted. Vertebral alignment was measured using fluoroscopy and muscle activity was recorded using surface and indwelling electrodes in 8 neck muscles. RESULTS: On average, tensed muscles increased cervical spine curvature and anterior motion of the cervical vertebrae relative to the torso. These changes, which were magnified by inversion, indicate that cervical intervertebral posture differs considerably between the relaxed and tensed states. CONCLUSION: Active muscle contraction can change the vertebral alignment in upright and inverted postures. This change in posture may alter the load path and injury mechanics during an axial head impact and may help explain the disparity between the neck injuries observed in real-world rollover accidents and ex vivo cadaver experiments. LEVEL OF EVIDENCE: N/A.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".