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Record W2052303482 · doi:10.3357/asem.2067.2007

Helicopter Cockpit Seat Side and Trapezius Muscle Metabolism with Night Vision Goggles

2007· article· en· W2052303482 on OpenAlexaff
Michael F. Harrison, J. Patrick Neary, Wayne J. Albert, D. Veillette, Neil P. McKenzie, James C. Croll

Bibliographic record

VenueAviation Space and Environmental Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCockpitAircrewMedicinePhysical medicine and rehabilitationAviation medicineBlood oxygenationAeronauticsEngineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Documented neck strain among military helicopter aircrew is becoming more frequent and many militaries use helicopters that provide pilots with the option of sitting in the left or right cockpit seat during missions. PURPOSE: The purpose of this study was to use near infrared spectroscopy (NIRS) to investigate the physiological changes in trapezius muscle oxygenation and blood volume during night vision goggle (NVG) flights as a function of left and right cockpit seating. METHODS: There were 25 pilots who were monitored during NVG flight simulator missions (97.7 +/- 16.1 min). Bilateral NIRS probes attached to the trapezius muscles at C7 level recorded total oxygenation index (TOI, %), total hemoglobin (tHb), oxyhemoglobin (Hbo2), and deoxyhemo-globin (HHb). RESULTS: No significant differences existed between variables for pilots seated in the right cockpit seat as compared with the pilots seated in the left cockpit seat in either trapezius muscle (pTOI = 0.72; ptHb = 0.72; pHbo2 = 0.57; pHHb = 0.21). CONCLUSION: Alternating cockpit seats on successive missions is not a means to decrease metabolic stress for helicopter pilots using NVG. This suggests that cockpit layout and location of essential instruments with respect to the horizontal and the increased head supported mass of the NVG may be important factors influencing metabolic stress of the trapezius muscle.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.351
Teacher spread0.335 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2007
Admission routes1
Has abstractyes

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