The Visual Midline and its Associations in Healthy Adults
Bibliographic record
Abstract
Abstract A visual midline (VM) shift may be experienced by some individuals post-stroke, but its associations with other variables are unclear. This study explored the relationships between the VM and other vision and non-vision variables in healthy adults. Normative data were calculated, and repeatability was assessed for each test. 93 participants without a history of visual or neurological impairment were recruited. Horizontal and vertical VM were measured using a VM gauge. During visit 1, the line bisection test (LBT) for spatial neglect, ocular dominance (OD), subjective straight-ahead (SSA), and visual open loop testing were also conducted. During visit 2, all assessments were repeated in Hong Kong while participants in Canada repeated VM measurements. Results showed no significant correlation between VM gauge measurements and other vision variables for either visit (P > 0.05). Age was significantly correlated with the absolute vertical VM during the first visit (Spearman’s rho = 0.47, P < 0.001), and with the absolute horizontal VM during the second visit (Spearman’s rho = 0.30, P = 0.004). LBT and OD tests had good repeatability (Limits of Agreement for Repeatability were 4 mm for LBT and 7 cm for OD). The visual open loop and SSA had poorer repeatability. This study revealed that VM is not associated with other vision variables in healthy adults. Therefore, it may be processed by independent neurological processing. This information is important for understanding the origins of VM shift, and normative data will aid clinicians in diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".