Do the ABCS 135 short cognitive screen and its subtests discriminate between normal cognition, mild cognitive impairment and dementia?
Bibliographic record
Abstract
BACKGROUND: Cognitive screening instruments are either too long for routine clinical use or not sensitive to distinguish mild cognitive impairment (MCI) from normal cognition (NC) or dementia. OBJECTIVE: To evaluate the sensitivity and specificity of the AB Cognitive Screen (ABCS) and its subtests with a view to improving its ability to differentiate between dementia, MCI and NC. The influence of age and education on sensitivity and specificity is also examined. DESIGN: Cross-sectional study. METHODS: Participants with dementia and MCI were recruited from those presenting to four specialty geriatric clinics in southern Ontario. Participants with NC were recruited from the family and friends of patients. A comprehensive geriatric assessment was done including ABCS, SMMSE and 15 point Geriatric Depression Scale. Analysis of variance and receiver operating characteristic (ROC) curves compared test scores. SMMSE scores were also analysed for comparison purposes. RESULTS: Three hundred and two participants had dementia, 166 had MCI and 174 had NC. ABCS total scores were significantly different between NC and MCI (mean difference 7.1, 1.8-12.5 CI, p = 0.000) while SMMSE scores were not (mean difference 0.5, -0.7-1.7, p < 0.628). Of individual ABCS subtests, verbal fluency and delayed recall were most sensitive to differences between NC and MCI. ROC curve analysis, which presents sensitivity and specificity, showed verbal fluency was better than delayed recall in distinguishing between NC and MCI, among participants 75 years of age or older. CONCLUSION: The AB Cognitive Screen (ABCS) can be administered in 3-5 min. The SMMSE and ABCS total and subtests significantly distinguished between dementia and MCI or NC. Verbal fluency and delayed recall were best at distinguishing between MCI and NC. The analysis illustrates how each subtest contributes to the sensitivity of the ABCS and suggests ways that sensitivity might be improved.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".