Is language impairment more common than executive dysfunction in amyotrophic lateral sclerosis?
Bibliographic record
Abstract
BACKGROUND: Systematic explorations of language abilities in patients with amyotrophic lateral sclerosis (ALS) are lacking in the context of wider cognitive change. METHODOLOGY: Neuropsychological assessment data were obtained from 51 patients with ALS and 35 healthy controls matched for age, gender and IQ. Composite scores were derived for the domains of language and executive functioning. Domain impairment was defined as a composite score ≤5th centile relative to the control mean. Cognitive impairment was also classified using recently published consensus criteria. RESULTS: The patients with ALS were impaired on language and executive composite scores. Language domain impairment was found in 43% of patients with ALS, and executive domain impairment in 31%. Standardised language and executive composite scores correlated in the ALS group (r=0.68, p<0.001). Multiple regression analyses indicated that scores on the executive composite accounted for 44% of the variance in language composite scores. CONCLUSIONS: Language impairments are at least as prevalent as executive dysfunction in ALS. While the two domains are strongly associated, executive dysfunction does not fully account for the profile of language impairments observed, further highlighting the heterogeneity of cognitive impairment in non-demented patients with ALS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".