Validity and reliability measures of the Swedish Karolinska version of the Edinburgh Cognitive and Behavioral ALS Screen (SK-ECAS)
Bibliographic record
Abstract
Cognitive and behavioral impairment is observed in up to 50% of patients with amyotrophic lateral sclerosis (ALS). The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is a 5-domain screening tool customized for quick cognitive screening in patients with ALS. Although the ECAS is available in Swedish at the Karolinska University Hospital (SK-ECAS), it has not yet been validated in Sweden stressing the need to assess validity and reliability of the SK-ECAS Version A. The study included 176 patients with ALS or other motor neuron disease diagnosed between September 2017 and October 2021 at the Karolinska ALS Clinical Research Center in Stockholm, Sweden, and 35 age-matched healthy control subjects. SK-ECAS was validated against the Montreal Cognitive Assessment (MoCA) and optimal cutoffs, receiver operating characteristic (ROC) curve and area under the curve (AUC) were calculated. We identified an optimal cutoff of 108 for the SK-ECAS total score and 82 for the SK-ECAS ALS-specific score to detect cognitive impairment. The SK-ECAS showed good performance in indicating abnormal cognition with an AUC of 0.73 for SK-ECAS ALS-specific score and 0.77 for SK-ECAS total score. There was good internal consistency with a Cronbach’s alpha of 0.79. This study demonstrates good validity and reliability indices for SK-ECAS Version A for the detection of cognitive impairment in newly diagnosed ALS patients.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".