Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) was developed by Dr Ziad Nasreddine in Montreal, Canada in 1995 for the detection of mild cognitive impairment (MCI) by health professionals. MCI is a syndrome defined as cognitive decline greater than expected for an individual’s age and education level but that does not interfere not ably with activities of daily life [1]. The prevalence of MCI in population-based epidemiological studies ranges from 3 to 19% in adults older than 65 years and more than half progress to dementia within 5 years. The MoCA may be useful in the occupational health setting for detecting MCI or early dementia especially as the workforce ages. A comprehensive website provides the test, instructions, normative data, references, frequently asked questions and permissions and updates [2]. The assessment consists of a 30 point test on a single side of A4 and can be administered in 10min. A score of 26 or above is considered normal. MoCA is free for use by universities, foundations, health professionals, hospitals, clinics and public health institutes. It is available in over 30 languages. The test may be administered by anyone who understands and follows the instructions but the website states that only a health professional with expertise in the cognitive field should interpret the results. In my opinion and experience, the MoCA is suitable and very useful as a convenient screening tool for use in the occupational health clinic by occupational health doctors and nurses. A less than satisfactory score would suggest the need for referral to a specialist for further assessment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".