Psychometric properties of the Russian version of the Edinburgh cognitive and behavioral amyotrophic lateral sclerosis screen
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition with observable cognitive and behavioral impairment. The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is a tool developed specifically for people with ALS (pwALS) and previously translated into Russian, but the psychometric properties have not yet been explored. The aim was to explore and determine the psychometric properties of the Russian-version of ECAS (ECAS-R). METHODS: 56 Russian speaking pwALS, 32 of their caregivers and 26 healthy controls were recruited for the study. They completed the ECAS-R, Patient Health Questionnaire-9 (PHQ-9) and Montreal Cognitive Assessment (MoCA). King Staging System was also utilized. Internal consistency, divergent and convergent validity, as well as culturally-derived cutoff scores of ECAS-R were determined. RESULTS: The internal consistency of ECAS-R was good (Cronbach's alpha = 0.73). Convergent validity was observed though a strong correlation between the ECAS-R and MoCA scores. No correlation between ECAS-R and PHQ-9 were observed in terms of divergent validity. Based on culturally-derived cutoff scores, 64.2% (N = 36) of pwALS displayed cognitive impairment, with the most affected cognitive domains as executive function and language. Apathy was the most common behavioral impairment for pwALS followed by a loss of sympathy/empathy. CONCLUSIONS: The ECAS-R is valid and reliable tool for the screening for the cognitive and behavioral impairment in Russian-speaking pwALS, with culturally-derived cutoffs presented.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".