IC‐P‐055: Mixed linear longitudinal modeling of biomarkers in ADNI
Bibliographic record
Abstract
We wished to investigate the longitudinal trajectory of AD biomarkers of amyloid beta deposition (e.g. CSF A β), and neurodegeneration (e.g. CSF total tau; FDG PET; MRI hippocampal volumes) in the ADNI pseudo-sporadic sample. This required the application of advanced statistical analysis via linear mixed models with fixed and random effects. The fixed effects model average population trajectories, while the random effects model that of individual subjects, given different slopes and intercepts. To determine the impact of sex as a confounding factor, we introduced it as a fixed effect in the model. We selected all 134 MCI subjects from the ADNI dataset who converted to probable AD within a timeframe of three years (48 female, age at baseline: 73.6 ± 7.2; 86 male, age at baseline: 75.2 ± 6.9), as well as 219 control subjects (106 female, age 76.2 ± 4.8; 113 male, age: 75.8 ± 5.3) from the same study. We ordered all subjects based on their score on the ADAS-Cog test as a surrogate marker of time, related to disease progression. We constructed the model used for analyzing the data in two levels (within-subjects and between-subjects model). Figure 1 shows mean and individual profiles for ADAS-Cog vs. CSF A β, CSF total tau, FDG PET and hippocampal volumes, for controls and MCI having progressed to probable AD, per sex. In all cases, there were significant y- intercept differences for control and MCI subjects. For all biomarkers, there was a significant y-intercept difference between males and females in the MCI population, but no difference in the mean regression slope, whereas this situation was only statistically significant in controls for pTau.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".