Bibliographic record
Abstract
This chapter outlines the test structure, administration and scoring guidelines, as well as the task demands of the Montreal Cognitive Assessment (MoCA). The validity and reliability of the MoCA is discussed, as well as the clinical applications and interpretations of the assessment. The MoCA is a neuropsychological assessment screener that is used to measure cognitive functioning and decline. The MoCA is recommended to be administered every three months or less and is normed for people ages 55-85 ( Nasreddine, 2021 ). The full-length exam, MoCA Full (version 8.1), is scored on a 30-point basis and can be administered in 10 minutes. The MoCA Full assesses short-term memory, visuospatial/executive functioning, attention, concentration, working memory, language, and orientation to place and time ( Nasreddine, 2021 ). The MoCA is useful in clinical settings to evaluate for and track cognitive decline amongst diverse populations, including Parkinson&s;s disease, dementia, and vascular cognitive impairments.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.022 |
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".