[P1–480]: CONVERSION OF SCORES FROM MMSE TO MOCA AND MOCA 5‐MINUTE PROTOCOL IN PATIENTS WITH STROKE OR TRANSIENT ISCHEMIC ATTACK
Bibliographic record
Abstract
Cognitive impairment is a rising healthcare burden in the rapidly aging population. A valid and reliable cognitive screen that is sensitive to early cognitive changes is essential for early identification of persons with cognitive impairment. The Mini-mental State Examination (MMSE) is insensitive to mild cognitive impairment (MCI) and cognitive changes with non-Alzheimer's etiology. The Montreal Cognitive Assessment (MoCA) has been shown to be valid as a cognitive screen for MCI, dementia and stroke. As recommended by the National Institute of Stroke and Neurological Disorders – Canadian Stroke Network Vascular Cognitive Impairment Harmonization standard, a shorter version, namely the MoCA 5-minute protocol, was also developed and validated as an ultra-quick cognitive screen for use at beside and over the telephone. Both tests are free for clinical and research use. The objective to this study is to convert scores from Cantonese version of the (CMMSE) to Hong Kong version of MoCA (HK-MoCA) and to examine the accuracy of the converted scores. The CMMSE and HK-MoCA were administered in 904 patients at 3 to 6 months after stroke or TIA. The dataset was randomly divided into 2 subsets: 70% data was used to train and 30% data was used to evaluate the performance of the trained models. Score conversion was performed using log-linear smoothing by equipercentile method and Poisson Regression taking into account the effects of age and education. Accuracy within 3-point error between the estimated and clinically administered HK-MoCA scores was calculated. 81.9% and 75.1% of the estimated HK-MoCA lie within 3-point error of the observed HK-MoCA using Poisson Regression and equipercentile methods, respectively. Scores on the HK-MoCA 5-minute protocol were calculated from the HK-MoCA. A score conversion table based on the log-linear smoothing by equipercentile method is shown in Table. Scores on the CMMSE were converted to HK-MoCA with high accuracy. We provide a table for converting scores from the CMMSE to HK-MoCA and HK-MoCA 5-minute protocol.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".