Alternate-Form Reliability of the Montreal Cognitive Assessment Screening Test in a Clinical Setting
Bibliographic record
Abstract
AIMS: The Montreal Cognitive Assessment (MoCA) has gained recognition for its validity in detecting cognitive impairment in several clinical populations. For serial assessments, alternate forms are needed to overcome possible practice effects. Our objective was to investigate the reliability of two German MoCA alternate forms for longitudinal assessment applications. METHODS: The original and one of two alternate forms of the MoCA were administered within a 60-min interval of a clinical interview in a counterbalanced order to 100 healthy elderly controls, 30 patients with mild cognitive impairment (MCI) and 30 patients with Alzheimer's disease (AD). The diagnosis of the majority of patients was supported by in vivo AD pathology biomarkers. RESULTS: There was a strong correlation between the alternate forms and the original MoCA in all groups, but particularly in the clinical samples. Total mean scores did not differ significantly between the MoCA versions, even taking into account the presentation order. As in previous studies, age and education influenced performance in the MoCA. The same pattern of group differences (controls > MCI > AD) was observed for each of the versions. CONCLUSION: All three forms can be reliably and interchangeably used in serial cognitive assessment, confirming the MoCA's applicability in research and clinical longitudinal approaches.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".