Longitudinal Feasibility of the Montreal Cognitive Assessment (MoCA) in Non-Demented ALS Patients
Bibliographic record
Abstract
INTRODUCTION: The present study aimed at testing the longitudinal feasibility of the Montreal Cognitive Assessment (MoCA) in an Italian cohort of non-demented amyotrophic lateral sclerosis (ALS) patients. METHODS: N = 39 non-demented ALS patients were followed-up at a 5-to-10-month interval (M = 6.8; SD = 1.4) with the MoCA and the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). Practice effects, test-retest reliability, and predictive validity (against follow-up ECAS scores) were assessed. Reliable change indices (RCIs) were derived via a regression-based approach by accounting for retest interval and baseline confounders (i.e., demographics, disease duration, and severity and progression rate). RESULTS: At retest, 100% and 69.2% of patients completed the ECAS and the MoCA, respectively. Patients who could not complete the MoCA showed a slightly more severe and fast-progressing disease. The MoCA was not subject to practice effects (t[32] = -0.80; p = 0.429) and was reliable at retest (intra-class correlation = 0.82). Moreover, baseline MoCA scores predicted the ECAS at retest. RCIs were successfully derived - with baseline MoCA scores being the only significant predictor of retest performances (ps < 0.001). CONCLUSIONS: As long as motor disabilities do not undermine its applicability, the MoCA appears to be longitudinally feasible at a 5-to-10-month interval in non-demented ALS patients. However, ALS-specific screeners - such as the ECAS - should be preferred whenever possible.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.000 |
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".