Reading Training with Threshold Stimuli in People with Central Vision Loss
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of a perceptual learning technique for improving reading performance of patients with central vision loss and to explore whether this learning generalizes to other visual functions. METHODS: Ten patients with central vision loss were trained binocularly, in four consecutive sessions, with serially presented words printed at each patient's reading acuity limit. Patients read 10 blocks of 100 words in each session. They were encouraged to read the whole word and were discouraged to read letter by letter. Assessment sessions before and after training measured fixation stability, monocular and binocular visual acuity, as well as reading acuity, critical print size, and maximum reading speed with continuous text. Another six patients with central vision loss were included in a test-retest control group and were tested twice, 1 week apart, with no intervention. RESULTS: The average time required to read a block of trials decreased significantly with each training session. After training, continuous text reading improved in terms of reading acuity (p = 0.017) and maximum reading speed (p = 0.01), but critical print size did not change. Binocular acuity improved significantly from an average of 0.54 logMAR before training to 0.44 logMAR after training. Binocular ratio (better eye acuity/binocular acuity) increased from an average of 1.0 before training to 1.17 after training. There was a 62% improvement in fixation stability in the better eye and 58% in the worse eye. There were no changes in the outcome measures for the test-retest control group. CONCLUSIONS: The technique described in this article can be used for vision rehabilitation of patients with central vision loss. When training is done with size threshold stimuli, learning generalizes to visual acuity, continuous text reading, and fixation stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".