Impact of white matter hyperintensities on disease progression in Progressive Supranuclear Palsy‐Richardson syndrome
Bibliographic record
Abstract
Abstract Background White matter hyperintensities (WMH) are recognized as neuroimaging biomarkers of cerebral small vessel disease; however, their clinical significance in Progressive Supranuclear Palsy‐Richardson syndrome (PSP‐RS) is poorly understood. Method 125 PSP‐RS patients from the Tilavonemab (ABBV‐8E12) clinical trial were assessed for disease severity using the PSP Rating Scale (PSPRS) at baseline and week 24. A PSPRS change index was calculated for week 24. WMH lesions were segmented on MRI FLAIR images using the Lesion Segmentation Tool and the total lesion volume (TLV) was calculated at baseline and week 24. The non‐laboratory Framingham Atherosclerotic Cardiovascular Disease Risk Score (FRS) was calculated in 99 patients. A linear mixed‐effect model for repeated measures was used to analyzed the TLV across the two time points. A multiple linear regression analyses was performed to analyze the association between baseline TLV, FRS, and their interaction in predicting the PSPRS change index. All analyses were adjusted for age, sex and disease duration. Result Mean age of the 125 PSP‐RS patients: 68.7 (49‐86) years, 51 females (40.8%). There was an increase of the TLV (B=0.09, 95% Confidence Intervals (CI):0.04‐0.13, p <0.001; mean±SD: 8.46±10.20 vs 9.38±10.60 ml) across time. Mean age of the 99 PSP‐RS patients with FRS: 66.8 (49‐74) years, 43 females (43.4%). Mean FRS: 20.6 ± 8.3%. A significant relationship was found between the TLV and the PSPRS change index (B=0.08, CI:0.01‐0.15, p = 0.02) and between the FRS and the PSPRS change index (B=0.11, CI:0.01‐0.21, p = 0.03). TLV*FRS shows a significant negative association with the PSPRS change index (B=‐0.01, CI:‐0.01‐ 0.00, p = 0.02). Conclusion TLV increased over time in PSP‐RS patients. For each 1 ml increase in TLV, the PSPRS change score increased by 0.11, and for each 1% increase in FRS, the PSPRS change score increased by 0.08. However, the increase must be adjusted for the combined effect, i.e., when both FRS and TLV are present, the increase is slightly smaller than the sum of their individual effects. *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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".