Validation of a Visual Midline Gauge: A Cross-Sectional Study
Bibliographic record
Abstract
Background This study standardized the parameters of a novel visual midline gauge and presents normative data, which can be used by optometrists in the assessment of visual midline. Methods Ninety-three participants from three age groups (18 to 44, 45 to 64, and > 65 years) without history of significant neurological or ocular problems were recruited in Waterloo, Canada and Hong Kong. In experiment 1, the perceived horizontal and vertical visual midline was measured using the gauge for 2 speeds and 2 repositioning methods. In experiment 2, the perceived midline was measured for three different distances (25, 50 and 100 cm) using a target speed and repositioning method chosen from the first experiment results. Since there was no significant difference between the two sites in any of the measures, data were combined for analysis. Results For experiment 1, linear mixed models showed no effect of age, speed or repositioning method and no interaction (p>0.05) for the perceived midline position (p>0.05). In experiment 2, there was no significant effect of age or distance and no interaction effects on the perceived horizontal and vertical visual midline position using the chosen speed and method (2.3 degrees per second, adjustment method). Normative data (mean and 95% ranges of the perceived visual midline for control participants) was tabulated. The measurements were found to be repeatable. A few participants were found to have significant midline shifts. Conclusion This study shows that the measurement of midline is tolerant of differences of target speed, testing method, test distance, and age group of the participants and that the measurements using the visual midline gauge are repeatable. It is possible for even individuals without a history of neurological or ocular disorders to have significant shifts in their perceived visual midline.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".