Anosognosia in Alzheimer’s Pathology: Validation of a New Measure
Bibliographic record
Abstract
OBJECTIVE: Two studies were performed to validate a brief measure of cognitive insight and compare it to an empirical model - the Cognitive Awareness Model (CAM). METHOD: A pilot study included 31 (52% male; Mage = 69.42) patients from an outpatient neuropsychological assessment clinic. Seven patients were diagnosed with likely Alzheimer's dementia (AD), 15 mild cognitive impairment (MCI), and 9 no diagnosis (i.e., cognitively normal; CN). The Cognitive Coding Form (CCF) and several other measures were administered. Study 2 entailed archival data extraction of 240 patients (80 CN, 80 MCI, and 80 AD; 53.3% female; Mage = 72.8) to examine whether the CCF predicts memory (Wechsler Memory Scale - IV) and executive functioning (Trail-Making Test B). RESULTS: The pilot study found preliminary evidence of convergent and discriminant validity for the 8-item CCF. Study 2 confirmed that both patient-reported cognitive concerns (F(2,237) = 10.40, p < .001, ω2 = .07, power = .99) and, more strongly, CCF informant-patient discrepancy scores (F(2,237) = 24.52, p < .001, ω2 = .16, power = .99) can distinguish CNs from those with MCI and AD. A regression indicated that depression (5.5%; β = -.38, p < .001) and TMT-B (13%; β = -.43, p < .001), together accounted for 18.5% of the variance in insight (R2 = .19, F(2,219) = 26.10, p < .001), supporting the CAM. CONCLUSIONS: These studies establish an efficient measure of insight with high clinical utility and inform the literature on the role of insight in predicting performance in those with Alzheimer's pathology.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".