Semantic deficits in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Our objective was to investigate, and establish neuroanatomical correlates of, semantic deficits in amyotrophic lateral sclerosis (ALS) and amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD), compared to semantic dementia (SD) and controls. Semantic deficits were evaluated using a naming and semantic knowledge composite score, comprising verbal and non-verbal neuropsychological measures of single-word processing (confrontational naming, comprehension, and semantic association) from the Sydney Language Battery (SYDBAT) and Addenbrooke's Cognitive Examination - Revised (ACE-R). Voxel based morphometry (VBM) analysis was conducted using the region of interest approach. In total, 84 participants were recruited from a multidisciplinary research clinic in Sydney. Participants included 17 patients with ALS, 19 with ALS-FTD, 22 with SD and 26 age- and education-matched healthy controls. Significant semantic deficits were observed in ALS and ALS-FTD compared to controls. The severity of semantic deficits varied across the clinical phenotypes: ALS patients were less impaired than ALS-FTD patients, who in turn were not as impaired as SD patients. Anterior temporal lobe atrophy significantly correlated with semantic deficits. In conclusion, semantic impairment is a feature of ALS and ALS-FTD, and reflects the severity of temporal lobe pathology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".