Tool for Screening Visual Acuity in Older Individuals With Dementia
Bibliographic record
Abstract
Rationale/Objective: To develop a screening and referral algorithm tool to help identify which older institutionalized individuals with dementia need an eye examination. METHODS: The visual acuity (VA) screening test was developed on an iPad retina display. Three optotypes were used (letters, numbers, and tumbling E's) to determine whether one works best with dementia. The screening VA results and algorithm decision were validated against those obtained by an optometrist performing a complete eye examination. RESULTS: Of the 150 participants, 14.7% did not respond to any optotype, while 85.3% responded to letters, 84.0% to numbers, and 66.0% to tumbling E's. The VA achieved was superior for letters. The concordance for the screening versus eye examination was >80% for VA and 90% for the algorithm. CONCLUSION: The results indicate that the tool was successful at identifying older individuals with dementia needing an eye examination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".