Biofeedback rehabilitation in patients with binocular inhibition due to macular disease
Bibliographic record
Abstract
BACKGROUND: To investigate whether patients with binocular reading inhibition due to central vision loss benefit from a new biofeedback (BF) rehabilitation method that aimed at improving fixation stability and at establishing a correspondence between the monocular preferred retinal loci (PRLs) on functioning retina in both eyes. METHODS: Thirty-three patients with bilateral macular disease and with binocular reading inhibition participated in 10 training sessions consisting of 10-min visual stimulation for each eye to stabilize fixation and relocate the PRL (if needed) using the BF module of the MP-1 microperimeter (Nidek Technologies Srl., Vigonza, PD, Italy). Binocular and monocular reading performance, contrast sensitivity, and visual acuity were evaluated pre and post training. Binocular summation/inhibition was evaluated with binocular ratio (BR). RESULTS: Fixation stability improved significantly post training in both eyes. Maximum reading speed during binocular viewing increased from 57 ± 24wpm pre training to 67 ± 24wpm post training. BR increased for all parameters of reading, visual acuity, and contrast sensitivity. Training resulted in a complete reversal of binocular reading inhibition in 30% of patients. CONCLUSIONS: For patients with binocular inhibition due to central vision loss, BF training to stabilize fixation and to bring the monocular PRLs into correspondence on functioning retina in both eyes is an efficient rehabilitation method to improve binocular performance.
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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) | 0.000 | 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".