Language impairment associated with prognosis in progressive supranuclear palsy
Bibliographic record
Abstract
BACKGROUND: Language impairment is common in progressive supranuclear palsy (PSP) and is often overlooked due to the severity of the motor symptoms. We investigated whether language can be used to predict PSP prognosis. METHODS: One hundred-forty-six patients with a diagnosis of possible or probable PSP from the Tilavonemab (ABBV-8E12) clinical trial were evaluated at baseline and week 32 using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the PSP rating scale (PSPRS) and the Schwab and England Activities of Daily Living Scale (SEADL). Percentage of change was calculated for each measure. Using correlations, we evaluated relationships between all RBANS-subscores (language, attention, memory and visuoconstructional), the PSPRS and SEADL scores at baseline; p-values were FDR corrected. Linear regression analyses were performed between the RBANS-language, RBANS-delayed-memory and RBANS-executive at baseline and the percentage of change over time. Grey matter volumes were extracted from three regions of interest based on language (bilateral temporal poles and inferior frontal gyri), executive (bilateral dorsolateral and superior frontal gyri and frontal pole) and memory (bilateral hippocampus, inferior, middle and superior temporal gyri) areas. RESULTS: Mean age of PSP patients: 68.8 years (49-86 years), 57 (39%) females. The RBANS language, executive and delayed memory scores at baseline were positively correlated with each other and with visuoconstructional and immediate memory score (all p<0.05). Only the RBANS-language score at baseline predicted percentage of change in PSPRS (B = -0.63, p = 0.003). The percentage of change in the RBANS-language score was predicted by the RBANS-language at baseline (B = -0.59, p<0.001). Lower age at baseline was associated with a worsening in language score over time (B = 0.51, p = 0.009). Language grey matter volume was associated with the change in RBANS-language score (B = 0.01, p = 0.02). CONCLUSIONS: language impairment at baseline, in contrast to memory and executive functions, was predictive of functional decline as measured by PSPRS. Atrophy in language areas at baseline predicted language decline. Language impairment may be an independent prognostic factor in PSP. *Based on research using data from AbbVie that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".