The impact of blurred vision on cognitive assessment
Bibliographic record
Abstract
The purpose of this study was to systematically assess the effect of blurred vision on several nonverbal neuropsychological measures commonly used as part of test batteries to assess the cognitive status of different patient populations. A total of 30 highly educated and healthy participants aged between 21 and 33 years were placed in one of three blurred vision groups, defined by their maximal visual acuity (20/20 or control group, 20/40, and 20/60). Blurred vision was simulated using positive diopters at a distance of 40 cm, the same distance as that at which tests were administered. Each participant was then assessed on a predetermined battery of nonverbal and verbal neuropsychological tests demanding different levels of acuity for optimal performance (i.e., tests whose items varied in terms of size and spatial frequency characteristics). In general, blurred vision significantly affected performance on nonverbal tests defined by small-sized/high-spatial-frequency items to a greater extent than on tests defined by larger sized/lower spatial-frequency items. As expected, blurred vision did not affect verbal test performance (Similarities, Information, and Arithmetic WAIS subtests). Our results are a clear indication of how even a "minimal" loss of visual acuity (20/40) can have a significant effect on the performance for certain nonverbal tests. In conclusion, such inferior performance is hypothetically interpretable as reflecting impaired cognitive functioning (i.e., attentional) targeted by a specific task (i.e., visual search) and suggests that the precision of the cognitive assessment and subsequent diagnosis are significantly biased when visuo-sensory abilities are not optimal, particularly for older patient populations where blurred vision resulting from correctable visual impairment is quite common.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".