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Record W2050424699 · doi:10.3357/asem.2841.2011

Neck Pain in Military Helicopter Aircrew and the Role of Exercise Therapy

2011· review· en· W2050424699 on OpenAlexaff
Danielle Salmon, Michael F. Harrison, J. Patrick Neary

Bibliographic record

VenueAviation Space and Environmental Medicine · 2011
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsAircrewNeck painMedicinePhysical therapyAviation medicinePhysical medicine and rehabilitationAeronauticsEngineeringAlternative medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.255
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations44
Published2011
Admission routes1
Has abstractyes

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