A LONGITUDINAL STUDY OF THE NAVIGATION PATTERNS OF DEMENTIA PATIENTS AND THEIR RELATIONSHIP TO MMSE
Bibliographic record
Abstract
Studies of the navigational patterns of assisted living facility residents with dementia have resulted in many insights into the progression of dementia e.g. more tortuous navigation has been associated with declining mental capability. In this pilot investigation, we found minute changes in navigational features such as speed, path-efficiency, angle-turn and, ambulation-fraction were predictive of cognitive function. In this study, navigational data of 10 subjects living in an assisted living facility were collected daily over a period of one year using an Ultra-wideband real-time location system with an accuracy of 20cm at 1Hz using a method described by Kearns et al. (2012), and compared with their cognitive status as measured by the Mini Mental State Exam. Six subjects had received clinical diagnoses of dementia with MMSE scores averaging 13.33 (SD=7.6) while the four control subjects’ MMSE averaged 18 (SD=9). We hypothesized that linear trends in the aforementioned features over a lengthy period might provide useful information concerning dementia’s progression. We employed linear contrast analysis to identify increasing and decreasing trends in the features and evaluated the change using one-way ANOVA to compare the trends within the two diagnostic groups. Two patients evidenced significant linear trends in angle-turn and path-efficiency with the maximum variability captured by angle-turn (14.7% and 11.7%). Both subjects were later found to have very low MMSE value (6 and 9 respectively). In four other residents angle-turn consistently increased over the 1-year monitoring interval suggesting that their cognitive abilities may have correspondingly deteriorated over this interval.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".