Impacts of ‘two-level’ variability on the differential power for Montreal Cognitive Assessment (MoCA) in prodromal dementia
Bibliographic record
Abstract
We read with great interest of the study defining and validating the screening accuracy of short form Montreal Cognitive Assessment (s-MoCA) in individuals with different cognitive status.1 This 5-min s-MoCA is attractive because it achieves the goal for adequate screening of cognitive impairment in an ageing population. Of particular interest, the ‘disease-specific’ versions of s-MoCA enrolling the items from full MoCA, present different classification accuracy. It is noteworthy to postulate that the alternative items between versions of s-MoCA may due to the underlying heterogeneity driven by two-level variability. The first-level is interindividual variability due to the diagnostic classification: mild cognitive impairment (MCI) and dementia refer to a highly heterogeneous community with diverse aetiology, cognitive profiles and clinical outcomes. It is not surprising to find some commonalities given that the underlying neural basis for cognitive impairment in patients with MCI may present with similar impaired cognitive domain …
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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 | 0.017 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".