Dynamic visual acuity (DVA) during locomotion for targets at near and far distances: Effects of aging, walking speed and head-trunk coupling
Bibliographic record
Abstract
This study examined effects of aging, head-trunk coupling (HTcoupling) and walking speed on dynamic visual acuity (DVA) at near and far viewing distances. Ten healthy participants were recruited in 3 groups; young: 20-33 years, Older1: 65-74 years, Older2: 75-85 years. The binocular DVA was measured while walking on a treadmill at 0.75 and 1.5 m/s speeds. The optotype display was placed at 0.5 m for NearDVA and at 3.0 m for FarDVA. On randomly selected trials, HTcoupling was achieved by using a collar. A mix-factor ANOVA (age-group x HTcoupling x speed) was performed separately for the Near and FarDVA. NearDVA declined with HTcoupling (p=0.021). Additionally, NearDVA worsened at the faster speed (p< 0.001). At 1.5 m/s speed the differences between Young and Older2 groups were significant (p=0.012) and those between Older1 and Older2 were marginal (p=0.085). FarDVA declined at the faster speed (p< 0.001) with no effect of HTcoupling or age-group. NearDVA is more sensitive to normal aging process. These age-related deficits become more apparent at higher walking speeds. Effect of HTcoupling on NearDVA suggests a possible additive effect of insufficient dampening of the vertical movement of the overall head-trunk complex and inability of the linear vestibulo-ocular reflex to compensate for the consequent high discrepancy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".