Post audio-visual biofeedback training visual functions and quality of life in paediatric idiopathic infantile nystagmus: A pilot study
Bibliographic record
Abstract
Purpose: Biofeedback training (BT) was adapted to idiopathic infantile nystagmus syndrome (IINS) cases to enhance visual functions and quality of life (QoL). Methods: 10 patients (age 9 ± 3.2 years) treated with the audio-visual BT module of the MAIA microperimeter (Centervue, Padova, Italy) were assessed in two baseline visits and 1week post-BT (BT 80 min in total). The outcomes were distance and near binocular best corrected visual acuity (BBCVA), fixation stability, reading speed, contrast sensitivity, stereopsis and Children’s Visual Function Questionnaire. One-way repeated measured ANOVA and paired t-tests were used. Results: Distance BBCVA improved from 0.46 ± 0.21 and 0.43 ± 0.18 pre-BT to 0.33 ± 0.2 logMAR post-BT ( F (2,27) = 13.75, p = 0.0002). Post-BT was better than baseline ( p = 0.0001) and pre-BT ( p = 0.001). Near BBCVA improved from 0.23 ± 0.09 and 0.21 ± 0.14 pre-BT to 0.04 ± 0.08 post-BT (F (2,27) = 22.12, p = 0.000014), post-BT was better than baseline ( p = 0.0001) and pre-BT ( p = 0.0006). Stereopsis improved from 283 ± 338″ to 39 ± 32.2″ ( p = 0.04), contrast sensitivity from 0.26 ± 0.17 to 0.08 ± 0.12 log units ( p = 0.01), and reading speed improved from 74.7 ± 51.2 wpm to 104.7 ± 53.6 wpm ( p = 0.0006). Fixation stability improved from 33.6 ± 28.1 to 14.3 ± 10.1 sq. QoL increased from 23.8 ± 2.2 to 26.3 ± 2.3 units ( p = 0.001). Conclusion: BT benefited all visual functions and QoL in this pilot study, heralding a new possibility for Low Vision Rehabilitation in IINS.
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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) | 0.001 | 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".