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Record W2410427836 · doi:10.1097/opx.0000000000000709

Head‐Eye Coordination Increases with Age and Varies across Countries

2015· article· en· W2410427836 on OpenAlexafffund
Frédéric J.A.M. Poirier, Guillaume Giraudet, Jocelyn Faubert

Bibliographic record

VenueOptometry and Vision Science · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsEssilor (Canada)Université de MontréalAssociation for Canadian StudiesUniversité du Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSaccadeEye movementHead (geology)Physical medicine and rehabilitationAudiologyPopulationOptometryPsychologyDemographyOphthalmologyMedicineGeology

Abstract

fetched live from OpenAlex

PURPOSE: Head movements in older people may contribute to their dizziness and equilibrium problems. Head gain is the ratio of head movement to total movement (head + eye) when executing a saccade to an eccentric target. Two studies have investigated the relationship between head gain and age but have provided conflicting results. METHODS: We report head gain data collected from research laboratories and optician stores. Our sample sizes are much larger (n = 657 for laboratory, n = 64,458 for optician stores), permitting more detailed analyses. RESULTS: The head-eye coefficient, expressed as 100 times the square root of head gain, was bimodal with one mode of primarily eye movers and one mode of eye-and-head movers. Head-eye coefficient increased with age and was invariant with eye correction and gender. We also found an effect of nation that seemed associated with gross domestic product or by latitude (in the northern hemisphere) and log population density. DISCUSSION: Assuming that head movements and visual distortions contribute to dizziness and equilibrium problems, our study suggests that customizing eyewear based on age and country may help in reducing the prevalence of problems associated with head and/or eye movements.

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)0.001
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 score0.065
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
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 head0.024
GPT teacher head0.415
Teacher spread0.391 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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