Comparison of Two Lighting Assessment Methods when Reading with Low Vision
Bibliographic record
Abstract
SIGNIFICANCE: Lighting assessments are an important component of low-vision rehabilitation but are rarely studied systematically. Our study indicates that preferred lighting levels support improvements in reading ease and enjoyment, independently of the evaluation technique. To determine preferred illumination level and color temperature, the LuxIQ is quicker to administer and covers broad options of settings. PURPOSE: The purpose of this study was to determine if preferred lighting, as assessed by the LuxIQ versus a standard lighting assessment, leads to better reading outcomes in individuals with low vision. METHODS: Preferred lighting was assessed at home with visually impaired persons (mean age, 75.3 years), using the LuxIQ (n = 18) or a standard technique based on trying out light bulbs of various intensity and color temperature (n = 16). Maximum reading speed and reading acuity were measured before the lighting intervention and then under the preferred lighting conditions. A 1-month telephone follow-up evaluated the (1) compliance with the lighting recommendations and, for those who modified their lighting, (2) their level of satisfaction with the length of reading time and eye strain felt during reading. RESULTS: Compared with usual lighting conditions, most participants preferred a cooler temperature at a higher illuminance level. Neither lighting assessment type appeared to lead to substantially improved objectively measurable reading outcomes. At the 1-month follow-up, 56% of the participants had modified their lighting, having a significant effect on satisfaction (P < .01), independent of assessment method. Of 18 respondents, 16 (87.5%) mentioned that reading was more enjoyable or easier with the lighting modifications. CONCLUSIONS: Both lighting assessment methods lead to comparable results, but the LuxIQ is easier and faster to use. More research is needed to determine whether the LuxIQ is suitable to be incorporated into clinical practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | medium |
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".