Vision therapy improves binocular visual dysfunction in patients with mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To evaluate benefits of binocular vision and ocular motility training in patients with long-term sequelae after mild traumatic brain injury (mTBI). METHODS: Twenty-eight mTBI (concussion) patients from 25 to 61 years of age with oculomotor dysfunction were selected by optometric examination. The vision therapy was designed to improve convergence, pursuit and saccades as well as to increase fusional reserves. The vision therapy was conducted by a neurooptometrist and a speech therapist, and took place weekly for 1 hour during 10 continuous weeks. Between vision training sessions, patients trained at home for 15-20 minutes daily. Before and after vision therapy, patients completed a test battery including the Groffman Visual Tracing Test, King-Devick test (K-D), a reading speed test, Multidimensional Fatigue Inventory (MFI-20) and patient interviews based on a modified version of the Canadian Occupational Performance Measure (COPM). RESULTS: Twenty-seven patients completed the vision therapy. After the therapy, improvements were measured on all test parameters, e.g. Groffman Visual Tracing Test (p < 0.05), K-D-Test (p = 0.01), Reading Speed Test (p < 0.01) and MFI-20 total (p < 0.05). The results for the modified COPM were significantly improved for both performance and satisfaction (0.0001 < p < 0.01). CONCLUSION: Vision therapy improved fixation stability and endurance. Reading speed measured by the numbers of saccades and regressions time consumption per read word increased. There was also an improvement in visual attention, possibly making patients safer in traffic and outdoor activities.
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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".