Norwegian version of the Edinburgh cognitive and behavioural ALS screen: Construct validity, internal consistency, inter-rater, and test-retest reliability
Bibliographic record
Abstract
BACKGROUND: Research collaboration highlight a need for validated tests in other languages than English. Translation and culture adjustments may threaten essential features of the original instrument. OBJECTIVE: To assess the internal consistency, inter-rater and test-retest reliability, and construct validity of the Norwegian version of the Edinburgh Cognitive and Behavioural Amyotrophic Lateral Sclerosis (ALS) Screen (ECAS-N). METHODS: Performance of 71 subjects with ALS, 85 healthy controls (HC) and 6 controls with Alzheimer's disease (AD) were assessed with the ECAS-N. Test-retest interval was four months. Internal consistency was evaluated using Cronbach's alpha; reliability was assessed using intraclass correlation coefficient (ICC), Cohen's kappa, and Bland Altman plot. Five hypothesis, including the Montreal Cognitive Assessment (MoCA) screen, was evaluated for construct validity. RESULTS: ECAS-N total score produced a Cronbach's alpha of 0.65, had excellent inter-rater reliability (ICC = 0.99) and acceptable test-retest reliability (ICC = 0.73). Construct validity analysis suggested valid use of the ECAS-N to distinguish people with ALS-specific cognitive impairment from HC (p = 0.001) and those with AD (p = 0.002). The MoCA and ECAS-N were moderately correlated (r = 0.53). CONCLUSION: The ECAS-N has potential to be used by different testers in clinical practice and research to screen patients with ALS who speak Norwegian and for documenting cognitive impairment over time.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".