P1‐444: BASELINE DATA IN A LONGITUDINAL VALIDATION OF COGNIGRAM<sup>TM</sup> COMPUTERIZED COGNITIVE TESTING IN MILD COGNITIVE IMPAIRMENT (MCI)
Bibliographic record
Abstract
Studies have shown that computerized cognitive assessments have the capacity for early detection of subtle changes in cognition in older adults by way of high-precision measurement of accuracy and speed of responses. The Cognigram (CG) platform is a computerized card game that measures cognitive functioning, with results shown to be unaffected by language, educational level, or cultural background. Our study aims to assess the capacity of CG to predict future cognitive decline in mild cognitive impairment (MCI) and cognitively normal (CN) subjects. Baseline data is presented here. Subjects were characterized by a full cognitive assessment at a tertiary memory clinic. 13 MCI and 8 CN subjects are presented here (target 30 MCI and 30 CN). CG testing and MoCA testing sessions will be performed at baseline, 3, 6, 9, 12, 24, and 36 months. Clinical and neuropsychological evaluations will be performed at baseline, 12, 24, and 36 months. MCI participants’ average age is 77.8 years old and 70.0 years old for CN. 23% of MCI and 100% of CN participants are female. The average baseline MoCA score for MCI was 22.2 and 28.13 for CN. The average baseline CG composite score for psychomotor function/attention for MCI was 85.0 and 97.0 for CN. The average baseline CG composite score for learning/working memory was 89.0 for MCI and 100.0 for CN participants. In this baseline data, both average CG composite scores for MCI participants were within the borderline performance range of 80-89, compared to the CN participants whose scores were within the normal performance range of 92-150. Preliminary findings to date show that CG can help distinguish cognitive differences between MCI and CN participants at baseline. The next three years of data collection will hopefully identify early changes in CG scoring that are predictive of further decline in the subset of patients with MCI that will convert to 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".