Geometry of navigation identifies genetic-risk and clinical Alzheimer’s disease
Bibliographic record
Abstract
ABSTRACT Recent research evidence demonstrates that the inability to orient oneself and navigate space is an early indicator of Alzheimer’s Disease. The video game Sea Hero Quest (SHQ) was designed to assess the players’ navigation ability, and several research works analysed the SHQ data using simple metrics such as length and time of navigation paths. Expanding these analyses, we propose new performance metrics that capture the geometry of paths, and analyse datasets of more than 60,000 navigators. The metrics identify players who failed the navigation task, the dementia patients, and carriers of the at-risk allele of the Apolipoprotein-E [APOE]. Furthermore the metrics detect weak navigation ability when only a fraction of navigation paths are used, with superior performance to baseline methods. Our findings demonstrate that the proposed performance metrics pave the way to a comprehensive pre-clinical screening toolbox for Alzheimer’s Disease. TEASER We propose geometric methods to capture decline in navigation ability from dementia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".