Edinburgh Cognitive and Behavioral Amyotrophic Lateral Sclerosis Screen (ECAS) in Norway: Protocol for validation and a prospective cohort study
Bibliographic record
Abstract
In amyotrophic lateral sclerosis (ALS) cognitive impairment may occur. This could detrimentally influence communication between patient and health-care professionals and make clinical assessment difficult. Given the short life expectancy after diagnosis, it is crucial to accurately identify ALS patients early. Although suitable cognitive screening tools for patients with ALS are available, they have not been evaluated in a Norwegian population. Interpretation of scores for available tests and practical application of scoring is also not well established. The protocol described here involves two related studies that aim to improve the quality of ALS clinical testing instruments used in the Norwegian population. The first is a validation study that evaluates the psychometric properties of the ECAS-Norwegian. The second is a prospective cohort study that evaluates the ECAS-Norwegian as a tool to predict early changes in ability to work, drive a car and the need for advanced therapy. Study 1 is a multicenter study using international quality criteria. Patients with ALS, healthy control subjects, and control subjects with dementia will be included. Primary outcome is ECAS-Norwegian scores. In study 2, patients with ALS will be included. ECAS-Norwegian compared to Clinical Dementia Rating score and Montreal Cognitive Assessment scores will be used as a prognostic tool for working, driving, and initiating advanced life-prolonging therapy. Before clinical implementation, the ECAS-Norwegian needs to be evaluated and validated. Successful validation and implementation of the ECAS-Norwegian may provide early identification of cognitive impairment in ALS, leading to more proactive, individualized treatment.
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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 | 0.053 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".