Vision Evaluation Tools for Adults With Acquired Brain Injury: A Scoping Review
Bibliographic record
Abstract
Background. Unrecognized visual deficits (VDs) following an acquired brain injury (ABI) may impact clients’ rehabilitation. Little is known about evaluation tools used in vision rehabilitation. Purpose. To systematically explore the literature describing evaluation tools used for VD on adults with ABI. Method. Using a scoping review methodology, we searched in MEDLINE(Ovid), Embase, CINAHL, PsycINFO, and the grey literature from inception to 2020. Quantitative and thematic analyses were performed. Findings. Of the 83 studies reporting on 86 evaluation tools, 47% used multiple tools to assess VD. Tools were mostly used by occupational therapists and psychologists to evaluate intermediate, intermediate to high, and high-level visual skills. Clinicians tend to select specific tools that focus on different levels of the hierarchy of visual skills. Implications. Future research should investigate the optimal timeframe for assessment of VD and the psychometric properties of tools to ensure comprehensive VD evaluation.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".