Adaptation and norming of the Edinburgh Cognitive and behavioural amyotrophic lateral sclerosis screen (ECAS) for three language groups in South Africa
Bibliographic record
Abstract
Objectives: To adapt and translate the Edinburgh Cognitive and behavioural amyotrophic lateral sclerosis screen (ECAS); to generate preliminary normative data for three language groups in South Africa (SA); to assess the convergent validity of the ECAS in SA samples. Methods: The ECAS was linguistically and culturally adapted for Afrikaans-, isiXhosa-, and English-speaking SA adults (n = 108, 100, and 53, respectively). Each language group was stratified by age and educational level. Cutoff scores for cognitive impairment were set at the group mean minus two standard deviations (SDs). A pilot sample of ALS patients and controls (n = 21 each) were administered the ECAS and an extensive neuropsychological evaluation (NPE) and the Montreal Cognitive Assessment (MoCA) to assess convergent validity. Results: Across the three language groups, the total ECAS cutoff scores ranged from 68 to 97. The ECAS score correlated significantly positively with educational level (p < 0.001) and negatively with age (p < 0.005). The restricted letter fluency task demonstrated a floor effect, particularly in Afrikaans-speakers. The mean total ECAS score (±SD) was similar in ALS patients (103.52 ± 11.90) and controls (100.67 ± 20.49; p = 0.58). Three (14.3%) ALS patients scored below the cutoff for cognitive impairment. Correlations between individual ECAS subtests and analogous NPE tests ranged from weak to moderate. The MoCA score was significantly positively correlated with the ECAS total score (r = 0.59; p = < 0.001). Conclusions: The adapted ECAS and associated normative data will aid cognitive screening of African ALS patients. Larger participant numbers are needed to assess the validity of the adapted instrument.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".