Test‐retest Variability of a Standardized Low Vision Lighting Assessment
Bibliographic record
Abstract
SIGNIFICANCE: Systematic lighting assessments should be part of low vision evaluations. The LuxIQ has gained popularity as an assessment tool, but its reliability has not been examined independently and is necessary for evidence-based vision rehabilitation. PURPOSE: Besides magnification, improved lighting levels are a common intervention in reading rehabilitation for individuals with low vision. Determining the appropriate lighting can be a complex and time-consuming task. The LuxIQ is a portable lighting assessment tool that can be used to systematically measure lighting preferences; however, there is little independent evidence to support its reliability in low vision rehabilitation. METHODS: One hundred nine control subjects (age, 18 to 85 years) and 64 individuals with low vision (age, 27 to 99 years) adjusted both the luminance and color temperature parameters on the LuxIQ while viewing a sentence on the MNREAD at their preferred print size for continuous reading. After 30 minutes, they were asked to repeat the same measurements. RESULTS: Using Bland-Altman plots, test-retest variability was calculated using the limits of agreement (LOAs). For illuminance, the LOA width was 2806 lux for control subjects and 2657 lux for visually impaired participants. For color temperature, the LOA width was 2807 K for control subjects and 2364 K for those with a visual impairment. Difference scores were centered near zero, indicating overall accuracy. CONCLUSIONS: The measurement of lighting preference lacks the precision necessary for clinical utility, given that the LOA for luminance ranged more than 2600 lux, with normally sighted and low vision participants. Such variability translates into a range of approximately ±40 or 50 W in an incandescent light bulb, depending on the luminance level, making it clinically difficult to narrow down the options for evidence-based lighting recommendations. Next steps are to examine whether the reading behavior of low vision clients is positively affected by interventions that are based on LuxIQ recommendations.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".