Validation of the individul MoCA test items as indicators of domain-specific cognitive impairment in geriatric population
Bibliographic record
Abstract
The Canadian population suffering from dementia has been estimated at 8% and the cost associated with it is remarkably high. Simple, validated cognitive screening tools, such as the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) are used in clinics to evaluate cognitive impairment with a total score. Just as test scores can identify patients with global cognitive impairment, we hypothesized that individual test item scores can identify cognitive impairment in different domains. In the present study, individual MoCA items were tested for their validity and clinical utility for prediction of domain-specific cognitive impairment. A total sample size of 185 patients who were tested on both the MoCA and neuropsychological tests were extracted from data collected at two geriatric outpatient clinics. Domains assessed by the neuropsychological tests were identified using a Principal Component Analysis. Validity of the individual MoCA items was demonstrated by comparing scores obtained from MoCA items and neuropsychological tests using bivariate correlations followed by multiple stepwise regressions. Significant, but weak-to-moderate correlations were seen between the MoCA items and neuropsychological tests. The strongest association (r = .46, p < .01) was seen between MoCA 5-word recall and performance in the memory domain. Some of MoCA items were significantly correlated to multiple cognitive domains. Predictive utility of the MoCA items for domain-specific cognitive impairment in clinical setting was tested using accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Items with a satisfactory level of accuracy (>70%) were: Date for memory, Repeat Sentence 2 for processing speed, Serial 7s Subtraction for visuospatial, and Repeat Sentences and Clock tasks for language. Most of the MoCA items were sensitive, but not specific. Additionally, all the items showed high NPVs, but poor PPVs. These findings suggest that the MoCA items that patients failed were not as informative as items that they passed.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".