A-018 Informant and Participant-Report of Functioning Predicts Performance on Objective Cognitive Screening
Bibliographic record
Abstract
Abstract Objective Montreal Cognitive Assessment (MoCA) and Alzheimer’s Disease 8 questionnaire (AD8) are widely used measures for clinical screening of dementia related disorders. Previous research on MoCA and AD8 has been solely focused on participant-report AD8 measures without consideration of informant reports. We hypothesize informant reported AD8 and participant MoCA scores will be inversely related, participant-reported AD8 will be weakly associated with MoCA performance, and informant reported AD8 will more reliably predictor cognitive performance. Methods Participants (N = 212) were seen from 2018 to 2020 through a free community screening service (Brain Health Check-In) at Banner Sun Health Research Institute in Arizona. First and second hypotheses were analyzed with Spearman’s Rho (r), third hypothesis utilized a linear regression. Results Both participant and informant reported AD8 directly correlated with overall cognitive performance classification (r = 0.639 [informant] confidence interval [CI] = .0552–0.712, p < .000; r = 0.610 [participant] CI = 0.518–0.688, p < .000). Informant reported AD8 ratings were significantly inversely correlated with MoCA performance (r = −0.497, p < .000). Participant reported AD8 ratings also inversely correlated with overall MoCA scores with a weaker association (r = −0.296, p < .000). Neither participant nor informant reported AD8 were able to reliably predict categorical cognitive performance classification, but informant reported AD8 (r = −.686, p < .000) did emerge as a reliable predictor of MoCA performance. Conclusion(s) This study extends and reaffirms prior research about AD8 and suggests both informant- and participant-reports are valuable; however, informant often provides more clinically useful information related to cognitive functioning.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".