Validation of the Addenbrooke’s Cognitive Examination-III for detecting vascular dementia in Iranian patients with stroke: A secondary data analysis
Bibliographic record
Abstract
The current study was conducted with the aim of evaluating the third version of Addenbrooke's Cognitive Examination (ACE-III), and exploring its diagnostic power for a sample of stroke patients in the Iranian population. This was a cross-sectional analytical study, in which 206 patients with stroke were compared with 200 normal individuals as the control group. The patients were diagnosed based on the findings of neuroimaging and clinical examination by a neurologist. ACE-III, Montreal Cognitive Assessment (MoCA), Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), and The Structured Clinical Interview for DSM-5 (SCID-5) were used to gather the data and assess the vascular dementia in the patients. Furthermore, Cronbach's alpha, Pearson correlation coefficient, discriminant function analysis, and the receiver operating characteristic (ROC) curve were used to respectively measure internal consistency, convergent validity, discriminant validity, sensitivity, specificity, and the cutoff point of ACE-III. Internal consistency of ACE-III was excellent (α = 0.92 - 0.95), and convergent validity was measured through calculating the correlation between the scores of ACE-III and MoCA, which was very high (r = 0.957, P < 0.0001). Moreover, overall classification accuracy of ACE-III revealed that it is able to differentiate 87% of patients with vascular dementia from other patients. The area under the ROC curve was found to be 0.84, and cutoff point was 45/46, at which sensitivity and specificity were obtained as 0.72 and 0.90, respectively. ACE-III is a rapid, inexpensive, and efficient tool for evaluating cognitive deficits in specialized neurology clinics to provide a clinical and differential diagnosis of vascular dementia after stroke.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".