Cognigram <sup>TM</sup> Computerized Cognitive Testing: Longitudinal Validation Study Over Four Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it