Impact of Different Operational Definitions on Mild Cognitive Impairment Rate and MMSE and MoCA Performance in Transient Ischaemic Attack and Stroke
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is at least as prevalent as dementia after transient ischaemic attack (TIA)/stroke and is increasingly recognised as an important outcome in observational studies and randomised trials. However, there is no consensus on how impairment should be defined, and numerous different criteria exist. Previous studies have shown that different criteria for cognitive impairment impact on prevalence rates in epidemiological studies. However, there are few data on how operational differences within established criteria (e.g. Petersen-MCI) affect measured impairment rates and the performance of short cognitive tests such as the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), particularly in cerebrovascular disease. We therefore evaluated the effect of different operational definitions on measured rates of Petersen-MCI and on reliability of short cognitive tests in patients with TIA and stroke. METHODS: Consecutive patients underwent the MMSE, MoCA and neuropsychological battery ≥1 year after TIA or stroke in a population-based study. MCI was defined using the Petersen method and subclassified as single or multiple domain, both with (original) and without (modified) subjective memory impairment. Different cut-offs (>1, >1.5 and >2 standard deviations, SD) on a given test relative to published norms were compared together with use of single versus multiple tests to define domain impairment. RESULTS: 91 non-demented subjects completed neuropsychological testing (mean age ± SD 69.7 ± 11.6 years, 54 male, 49 stroke) at a mean of 3.1 ± 1.9 years after the index event. Rates of cognitive impairment ranged from 14/91 (15%) for MCI-original at >2 SD cut-off to 61/91 (67%) MCI-modified at >1 SD cut-off, and the proportion of MCI that was multiple domain varied, e.g. 24/46 (52%) versus only 5/27 (20%) at 1 versus 2 SD cut-off for MCI-modified. Requirement for subjective memory complaint approximately halved estimates [e.g. 17 (19%) vs. 39 (43%) for MCI at 1.5 SD cut-off, single test definition], whereas use of multiple tests versus a single test to define a cognitive domain had less impact. In general, diagnostic accuracy was higher, and optimal cut-offs lower, on MMSE and MoCA for multiple-domain versus single-domain MCI, but the MoCA appeared superior for detecting MCI-modified, whereas the MMSE performed well in detecting MCI-original. CONCLUSION: Even within established criteria for MCI, differences in operational methodology result in 4-fold variation in MCI estimates. Optimal MMSE and MoCA cut-offs are lower, and reliability more similar, when criteria for MCI are more stringent. Our findings have implications for sample size and adjusted relative risk calculations in randomised trials and for comparisons between studies.
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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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".