Errorless-Based Techniques Can Improve Route Finding in Early Alzheimer's Disease: A Case Study
Bibliographic record
Abstract
Topographical disorientation is a common and early manifestation of dementia of Alzheimer type, which threatens independence in activities of daily living. Errorless-based techniques appear to be effective in helping patients with amnesia to learn routes, but little is known about their effectiveness in early dementia of Alzheimer type. A 77-year-old woman with dementia of Alzheimer type had difficulty in finding her way around her seniors residence, which reduced her social activities. This study used an ABA design (A is the baseline and B is the intervention) with multiple baselines across routes for going to the rosary (target), laundry, and game rooms (controls). The errorless-based technique intervention was applied to 2 of the 3 routes. Analyses showed significant improvement only for the routes learned with errorless-based techniques. Following the study, the participant increased her topographical knowledge of her surroundings. Route learning interventions based on errorless-based techniques appear to be a promising approach for improving the independence in early dementia of Alzheimer type.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".