DEVELOPMENT AND EVALUATION OF COGNITIVE IMPAIRMENT SCREENING TOOL BASED ON EYE-TRACKING TECHNOLOGY
Bibliographic record
Abstract
Abstract Objectives This study aimed to develop a cognitive impairment screening tool based on eye tracking technology (ET-CIS), and apply ET-CIS to community-dwelling older adults to evaluated its screening performance. Methods ET-CIS were developed based on through systematic review, pre-experiments and expert consultation. We recruited older people in the community from July 2022 to November 2022 and collected data including demographics data, Montreal cognitive assessment, the mini-mental state examination, ET-CIS. The t-test and correlation analysis were conducted to screen out statistically significant parameters with of ET-CIS. The screening models were constructed using standardized score assignment, binary logistics regression analysis, and decision tree model in the training set, and were applied in the validation to evaluate the screening performance. The best model was selected as the final scoring model. Results ET-CIS was constructed including applicable objects, pre-assessment preparation, screening dimensions, specific experiments and parameters. A total of 301 subjects were included, including 163 in the cognitively normal group and 138 in the cognitive impairment group. The results showed that the decision tree model results showed that the sensitivity of the training set was 0.752 and the sensitivity of the validation set was 0.818. We use the decision tree model as the final model. Conclusions In this study, ET-CIS was developed including the evaluation of memory function, executive function, visuospatial function and abstract function. The screening model of ET-CIS in community-dwelling older people showed good discrimination, which demonstrated it could be used to effectively screen cognitive impairment in the community in the future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".