<p>Psychometric Properties of Cognitive Assessment in Amyotrophic Lateral Sclerosis: A Systematic Review</p>
Bibliographic record
Abstract
PURPOSE: We aimed to list all tests used to assess cognitive change in patients with amyotrophic lateral sclerosis (ALS) and to provide a descriptive synthesis of the psychometric properties of tests that were evaluated in a population of ALS patients. MATERIALS AND METHODS: The protocol is registered in PROSPERO (ID: CRD42017055603). We systematically search for literature in 11 databases. Full-text articles, in any language, with original research were included. All included articles were scrutinised by two independent authors. Disagreement was resolved by consensus. The framework of Lezak informed conceptualises of the tests identified. To evaluate methodological quality, we used the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN). Data were synthesised using criteria proposed by the Cochrane Back Review Group. RESULTS: Of 319 included articles, 46 articles reported information on the psychometric properties of cognitive tests used in patients with ALS. We found that the highest level of evidence was supported for the Reading the Mind in the Eye Test (RME), Addenbrooke's Cognitive Evaluation (ACE) and Frontal Assessment Battery (FAB). Moderate level of evidence was found for the screening tests; Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and the Montreal Cognitive Assessment (MoCA). CONCLUSION: The screening test, ECAS and the social cognition test, RME, may have some advantages over other tests that have been used for assessing cognitive change in ALS patients. Recommendations of ALS-specific tests with sound psychometric properties are urgently needed.
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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 | 0.036 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".