Do Reading Additions Improve Reading in Pre‐presbyopes with Low Vision?
Bibliographic record
Abstract
PURPOSE: This study compared three different methods of determining a reading addition and the possible improvement on reading performance in children and young adults with low vision. METHODS: Twenty-eight participants with low vision, aged 8 to 32 years, took part in the study. Reading additions were determined with (a) a modified Nott dynamic retinoscopy, (b) a subjective method, and (c) an age-based formula. Reading performance was assessed with MNREAD-style reading charts at 12.5 cm, with and without each reading addition in random order. Outcome measures were reading speed, critical print size, MNREAD threshold, and the area under the reading speed curve. RESULTS: For the whole group, there was no significant improvement in reading performance with any of the additions. When participants with normal accommodation at 12.5 cm were excluded, the area under the reading speed curve was significantly greater with all reading additions compared with no addition (p = 0.031, 0.028, and 0.028, respectively). Also, the reading acuity threshold was significantly better with all reading additions compared with no addition (p = 0.014, 0.030, and 0.036, respectively). Distance and near visual acuity, age, and contrast sensitivity did not predict improvement with a reading addition. All, but one, of the participants who showed a significant improvement in reading with an addition had reduced accommodation. CONCLUSIONS: A reading addition may improve reading performance for young people with low vision and should be considered as part of a low vision assessment, particularly when accommodation is reduced.
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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 | 0.001 | 0.005 |
| 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".