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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".