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Record W1995065060 · doi:10.3357/amhp.4027.2015

Night Vision Goggle-Induced Neck Pain in Military Helicopter Aircrew: A Literature Review

2014· review· en· W1995065060 on OpenAlexaff
Michael F. Harrison, Brendan Coffey, Wayne J. Albert, Steven L. Fischer

Bibliographic record

VenueAerospace Medicine and Human Performance · 2014
Typereview
Languageen
FieldMedicine
TopicOcular and Laser Science Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAircrewAviation medicineMedicineNight visionNeck painAeronauticsSpace medicineMilitary personnelPhysical medicine and rehabilitationEngineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Neck pain occurs at a significant rate in the military helicopter community. It is often attributed to the use of night vision goggles (NVG) and to a number of additional factors such as anthropometrics, posture, vibration, mission length, physical fitness, and helmet fit or load. A number of research studies have addressed many aspects of this epidemic, but an up-to-date and comprehensive review of the literature is not currently available. This paper reviews the spinal anatomy in general and then summarizes what is known about the incidence and prevalence of neck injuries, how the operational environments and equipment may contribute to these injuries, and what can be done to address them from a prevention and/or rehabilitation perspective. Harrison MF, Coffey B, Albert WJ, Fischer SL. Night vision goggle-induced neck pain in military helicopter aircrew: a literature review.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.038
GPT teacher head0.387
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations61
Published2014
Admission routes1
Has abstractyes

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