Conversion between Mini‐Mental State Examination and Montreal Cognitive Assessment scores in older adults undergoing selective surgery using Rasch analysis
Bibliographic record
Abstract
AIMS: To develop and validate a conversion table between the MMSE and the MoCA using Rasch analysis in older adults undergoing selective surgery and examine its diagnostic accuracy in detecting cognitive impairment. DESIGN: Cross-sectional study. METHODS: Older patients [N = 129; age 66.0 (4.6) years, education 7.7 (3.5) years] undergoing elective surgery were recruited from December 2017 to June 2018. All participants completed the MMSE and MoCA and 113 of them completed a battery of neuropsychological tests. Common person linking based on Rasch analysis was performed to develop the conversion table. The conversions were validated by calculating the intraclass correlation coefficient (ICC), score differences between actual and converted scores, and root mean squared error of the difference (RMSE). The diagnostic accuracy of the conversions for detecting cognitive impairment was also tested. RESULTS: The MoCA [person measure: 1.3 (1.1) logits] was better targeted to the patients than the MMSE [person measure: 3.2 (1.3) logits]. Conversion from MoCA to MMSE scores (ICC 0.84, 95% CI 0.77-0.88; RMSE 1.36) was more precise than conversion from MMSE to MoCA (ICC 0.82, 95% CI 0.75-0.87; RMSE 2.56). Conversion from MoCA to MMSE demonstrated better diagnostic accuracy in detecting cognitive impairment than the actual MMSE, whereas conversion from MMSE to MoCA exhibited the opposite pattern. CONCLUSION: Conversion from MoCA to MMSE was more precise and had better diagnostic accuracy in detecting pre-operative cognitive impairment in older patients undergoing selective surgery than conversion from MMSE into MoCA. IMPACT: The finding is useful for interpreting, comparing, and integrating cognitive measurements in surgical settings and clinical research. Statistically sound conversion between MoCA and MMSE based on Rasch analysis is now possible for surgical setting and clinical research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".