Use of white matter reference regions for detection of change in florbetapir positron emission tomography from completed phase 3 solanezumab trials
Bibliographic record
Abstract
INTRODUCTION: We compared subject-specific white matter (SSWM) and whole cerebellum (CBL) reference regions for power to detect longitudinal change in amyloid positron emission tomography signal. METHODS: Positive florbetapir positron emission tomography scans were analyzed from participants (66 placebo treated and 63 solanezumab treated) with mild dementia caused by Alzheimer's disease from the EXPEDITION and EXPEDITION2 studies. For comparison to CBL, a second normalization was performed on longitudinal data using an SSWM correction factor (SSWM normalization ratio [SSWMnr]). Analysis of covariance assessed baseline to 18-month change between treatment with solanezumab and placebo. Sample and effect size estimations provided magnitude of observed treatment changes. RESULTS: Longitudinal percent change between placebo and solanezumab using CBL was not significant (P = .536) but was significant for SSWMnr (P = .042). Compared with CBL, SSWMnr technique increased the power to detect a treatment difference, more than tripling the effect size and reducing the sample size requirements by 85% to 90%. DISCUSSION: Adjusting longitudinal standardized uptake value ratios with an SSWM reference region in these antiamyloid treatment trials increased mean change detection and decreased variance resulting in the substantial improvement in statistical power to detect change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".