Cervical Spine Motion Requirements From Night Vision Goggles May Play a Greater Role in Chronic Neck Pain than Helmet Mass Properties
Bibliographic record
Abstract
BACKGROUND: Chronic Neck Pain (CNP) among rotary-wing aircrew is thought to stem from night vision goggles (NVG) and counterweight (CW) systems which displace the centre of mass of the head. This investigation aimed to quantify the loads acting on the neck as a function of movement magnitude (MM), helmet conditions, and movement axes in rapid movements. METHODS: Cervical spine kinematics during rapid head repositioning tasks for flexion-extension (FE) and axial rotation (AR) movements were measured from 15 males and 15 females. Participants moved in either a 35° (Near MM) or 70° arc (Far MM), while donning a helmet, helmet with NVG, helmet with NVG and a typical CW, and a CW Liner (CWL). Measured EMG from three muscles bilaterally and used to drive a biomechanical model to quantify the compression and shear acting at the C5-C6 joint. RESULTS: In AR, the NVGs were associated with the largest compression magnitudes, 252 (24) N. CW conditions decreased the maximum compression to 249 (53) N. For FE, the compression was 340 N for the Far MM trials and 246 N for Near MMs. Changing the helmet configuration only modestly influenced these magnitudes in FE. CONCLUSION: Every 30° of MM increased compression by 57 to 105 N. The reduction of the moment of inertia by 16% in the CWL did not reduce reaction forces. Joint loads scaled proportionately with head-supported weight by a factor of 2.05. The magnitudes of loads suggest a cumulative loading pathway for CNP development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".