Cognigram <sup>TM</sup> Computerized Cognitive Testing: Longitudinal Validation Study Over Four Years
Bibliographic record
Abstract
Abstract Background As the population ages, there is increasing need for biomarkers which can predict if individuals will worsen cognitively or remain stable. Cognigram™ (CG) is a computerized cognitive battery which uses card‐sorting tasks to assess cognitive abilities, thus acting as a “digital biomarker”, which may predict cognitive decline earlier than paper‐based cognitive tests. Method Participants with clinically‐diagnosed mild cognitive impairment (MCI) were asked to complete CG testing and traditional neuropsychological testing over a period of four years. Individuals who demonstrated a significant deterioration on cognitive and functional testing over time (as determined by blinded expert consensus conference) were labelled as “decliners”. Those who did not deteriorate on testing were labelled as “non‐decliners”. CG findings were analyzed for early predictive changes which could differentiate between decliners and non‐decliners. Result Seventeen (M=12, F=5) individuals were identified for this analysis, and were classified over an average of 34 months in the study (range 6‐61 months) as decliners ( n = 8, 37.5% female, average age 75.6) or non‐decliners ( n = 9, 22.2% female, average age 75.4). Over the first year, the CG one‐back task (“Is this card the same as the previous card?”) consistently showed the decliner group having more negative change from baseline at each timepoint compared to the non‐decliner group, with moderate effect sizes observed at 3 and 9 months and a very large effect size observed at 12 months (Hedges’ g=‐1.54, p = 0.03). Conclusion The CG one‐back task showed greater negative change over one year in participants observed to decline clinically during the study. A larger study is needed to determine if CG can predict the likelihood of long‐term cognitive decline in patients with mild cognitive impairment.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".