P1‐354: Self and Error Awareness in Mild to Moderate Alzheimer's Disease
Bibliographic record
Abstract
A new touchscreen psychometric measure, the Error Awareness Dot Task (EADT), was developed in order assess levels of both error and self-awareness in mild to moderate Alzheimer's disease. 30 participants with a diagnosis of Alzheimer's disease were recruited for the current research project. Each participant completed a short psychometric battery which included the Clock drawing task, the Montreal Cognitive Assessment and the Patient Competency Rating Scale (PCRS; Prigatano & Klonoff, 1998). A carer or significant other was also required to complete the PCRS in order that a discrepancy score, with regard to the patient's ability to carry out activities of daily living (ADL's), could be calculated. Patients then completed three blocks of the Error Awareness Dot Task (EADT) on a touchscreen device; the task employs a Go/No-Go paradigm that requires participants to signal errors of commission via a motor response (screen press). The Error Awareness Dot Task proved a successful tool in measuring error awareness in Alzheimer's disease. Our results also show that levels of error awareness are significantly lower in those with a diagnosis of AD than cognitively healthy individuals. Importantly results garnered from the EADT also correlate with scores on standardized psychometric tests. Those with AD have significantly lower levels of error awareness than cognitively healthy individuals. The EADT has the potential to provide clinicians with a measure of 'online' error awareness thus adding a new tool to the existing psychometric battery.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".