Reading with central vision loss: binocular summation and inhibition
Bibliographic record
Abstract
PURPOSE: There are conflicting reports as to whether there is a binocular advantage or disadvantage when reading with central vision loss. This study examined binocular reading summation in patients with macular degeneration. METHODS: Seventy-one patients with bilateral central vision loss [mean age: 63 (S.D. = 21) years] participated. Reading performances during binocular and monocular viewing with the better eye (i.e., the eye with the best monocular visual acuity) were evaluated using different versions of the Italian MNREAD reading chart (www.precision-vision.com). Fixation stability and preferred retinal loci (PRLs) were recorded monocularly for each eye. The overall sample was split into inhibition, equality, and summation groups based on the binocular ratio (i.e., binocular/monocular) of the maximum reading speed. RESULTS: 41% of patients experienced binocular inhibition, 42% summation, and 17% equality. Binocular reading speed of the inhibition group was approximately 30 words per minute slower than those of the equality and summation groups, although the inhibition group had the best visual acuity. These patients generally had monocular PRLs in non-corresponding locations temporal or nasal to the scotoma, had the largest interocular acuity difference and lacked residual stereopsis. The three groups did not differ in fixational control, contrast sensitivity or critical print size. CONCLUSIONS: Equal proportions of patients with central vision loss show binocular reading summation and inhibition. Patients with binocular reading inhibition have poorer reading performance and different clinical characteristics than those with binocular reading summation and equality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".