Neck Pain in Military Helicopter Aircrew and the Role of Exercise Therapy
Bibliographic record
Abstract
Neck pain is a growing aeromedical concern for military forces on an international scale. Neck pain prevalence in the global military helicopter community has been reported in the range of 56.6-84.5%. Despite this high prevalence, historically, research examining helicopter aircrews has focused predominantly on low back pain. A number of recent studies have emerged examining flight-related factors that are hypothesized to contribute to the development of flight-related neck pain. Loading factors such as the posture adopted during flight, use of night vision goggles, and vibration have all been found to contribute to neck pain and muscular fatigue. Prolonged or repeated exposureto these loading factors has been hypothesized to perpetuate or contribute to the development of neck pain. Despite the high number of helicopter aircrew personnel that suffer from neck pain, very few individuals seek treatment for the disorder. The focus of medical personnel should, therefore, be directed toward a solution that addresses not only the issue of muscular fatigue, but the hesitancy to seek treatment. Previous research in military and civilian populations have used exercise therapy as a treatment modality for neck pain and have found improved endurance capacity in the neck musculature and reduced self-reported 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".