Supplementary Material for: Association between Montreal Cognitive Assessment Sub-Item Scores and Corresponding Cognitive Test Performance in Patients with Frontotemporal Dementia and Related Disorders
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA), a brief screening test developed to detect patients with mild cognitive impairment, is used in clinical settings across North America [Nasreddine et al.: J Am Geriatr Soc 2005;53:695-699]. The MoCA has been demonstrated to be sensitive to cognitive deficits in frontotemporal dementias (FTD) and related disorders [Coleman et al.: Alzheimer Dis Assoc Disord 2016;30:258-263]. Given attentional impairments in patients with FTD, whether and to what extent the abbreviated items on the MoCA may predict performance on corresponding assessments is not known. Testing and demographic data were extracted from a clinical database using a sample of 91 patients with FTD and related disorders. The relationship between MoCA items and corresponding neuropsychological tasks was assessed through McNemar tests and Spearman correlations. While some MoCA items such as letter fluency, orientation, and clock drawing were strongly correlated with the corresponding standard cognitive test, the MoCA trails were insensitive to impairment compared to the full Trail Making B Test (p = 0.01). In contrast, MoCA naming and delayed recall sub-items detected cognitive impairment more frequently than available comparison tests. The MoCA is a sensitive screening measure to detect impairment in patients with FTD and related disorders, but cognitive deficits specific to FTD result in differential performance on MoCA items compared to longer standard cognitive tests.
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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 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.838 | 0.218 |
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".