Cervical Spine Rotation and Range of Motion: Pilot Measurements During Driving
Bibliographic record
Abstract
OBJECTIVE: Previous studies have evaluated the cervical range of axial rotation during simulated driving conditions. The goals of this pilot study were to describe cervical spine rotation during in-car driving and determine the percentage of time outside neutral neck rotation and peak cervical axial rotation angles that the subjects adopted during various driving conditions. METHODS: Subjects drove around a specified route through the city of Guelph, Ontario, which included residential, thruway, and highway driving; additional minor driving tasks, such as lane changes, were also included. The cervical range of motion was measured continuously throughout the drive using an electromagnetic sensor; we also used videotape to document the specific driving tasks. RESULTS: The subjects spent 87.0 percent (SD=8.8) of time with their cervical spine in the neutral axial rotation position (±15 degrees). The percentage of time that the subjects spent outside of the neutral range of cervical axial rotation depended upon the driving section (including residential, thruway, and highway), and driving task being performed (starts, stops, and lane changes). The subjects spent a significantly greater proportion of time with their necks rotated beyond neutral during residential driving compared to thruway and highway driving (19.1% SD=8.3 vs. 10.7% SD=9.5 and 9.3% SD=8.7, respectively; p<.001). During driving, the peak angles of cervical axial rotation were an average of 35.7 degrees (SD=14.2) left and 42.5 degrees (SD=18.0) right. CONCLUSIONS: We observed a large degree of variability in cervical axial rotation during driving. We observed that most of the driving tasks related to stopping had increased proportion of time out of neutral rotation. Also, right-hand lane changes increased time out of neutral rotation more than left-hand lane changes. Drivers routinely adopt nonneutral head positions (on average 13% of the time); this is likely not enough to lead to injury.
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 | 0.001 | 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".