Neuropsychological assessment practices in PRECISION-ALS: challenges and opportunities for harmonization
Bibliographic record
Abstract
OBJECTIVE: To gather comprehensive insights regarding current neuropsychological assessment practices in PRECISION-ALS, a pan-European research and industry consortium, to propose areas which can be harmonized and facilitate more robust cross-country comparisons. METHODS: Representatives from PRECISION-ALS sites were surveyed with a semi-structured interview, gathering information on how people with ALS are assessed for cognitive/behavioral change, including how they are initially screened, classified as impaired/unimpaired, and followed up longitudinally. Assessment practices across PRECISION-ALS sites were summarized using descriptive analysis. RESULTS: Ten of the eleven PRECISION-ALS sites perform cognitive and/or behavioral screening at least once during the course of the disease, using the Edinburgh Cognitive and Behavioral ALS Screen, either for clinical or research purposes. All centers categorize impairment, but differ how it is defined, with some using local norms, and others using other countries' norms. Most sites account for age and education, but differ in how these factors are considered. Longitudinal protocols vary in terms of the number of assessments, time intervals, and use of alternative versions. Behavioral screening is more consistently implemented, with the ECAS caregiver interview as the standard tool, however there is a lack of clarity in how this data is applied. Many sites supplement cognitive and behavioral screening with additional measures of mood and/or neuropsychiatric symptoms. CONCLUSIONS: These findings illustrate areas of commonality and divergence in neuropsychological screening practices. Site-specific variations are likely to confound research involving cross-country data-sharing. PRECISION-ALS, in generating prospective population-based datasets, will provide agreed harmonized protocols for neuropsychological assessment across participating sites.
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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 | 0.364 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".