Visual outcomes of audio-luminous biofeedback training for a child with idiopathic nystagmus
Bibliographic record
Abstract
Microperimetry biofeedback training is a vision rehabilitation method that involves the training of attention and oculomotor control, and the rehabilitation of poorly located and non-functional preferred retinal loci. It can significantly improve distance and near visual acuity in age-related macular degeneration. Previous studies have shown that biofeedback training using electrical nystagmography can reduce nystagmus amplitude and increase foveation time. However, these improvements have not been sustained following training sessions. We hereby report a pediatric case of idiopathic nystagmus in an 11-year old patient treated with microperimetric biofeedback to improve visual acuity and fixation stability. The training had a beneficial impact, positively affecting fixation stability as well as distance and near reading vision. Subjectively, improvement in quality of life was also reported. Conversely to previous studies, the positive effects in this case were maintained for as long as twelve months following therapy. To the best of our knowledge, this is the first case with long-term benefits to be reported in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".