P1–147: A quadratic, P‐spline, mixed model to predict cognitive decline in Alzheimer's disease
Bibliographic record
Abstract
Deciphering the longitudinal trajectory of AD biomarkers would enhance our capacity for early diagnostic and prediction of disease progression. Our purpose was to study hippocampal volumes (HC) and FDG-PET metabolism trajectories based on either MMSE or ADAS-Cog scores as putative markers of clinical disease progression. Assuming the biomarker hypothesis proposed by Jack et al., we thus elicited to use a smoothing spline mixed effects model to predict the evolution of longitudinal biomarkers as a function of neuropsychological scores. We selected a total of 734 subjects who had at least two visits within a 36-month follow-up from ADNI: 167 probable AD patients, 136 MCI having progressed to probable AD within that timeframe (pMCI), 224 MCI subjects that remained clinically stable (sMCI), and 207 controls that remained clinically stable (sCRT). We extracted MMSE, ADAS-cog, HC volumes and FDG PET metabolism from the ADNI database. For modeling purposes we used the average of left and right HC volumes. We used a smoothing quadratic spline mixed effects model to describe and evaluate disease progression, from sCRT to sMCI, to pMCI and finally AD subjects. Cut-off points for each group, where the random effect was defined, were chosen as the mean MMSE and ADAS-Cog score per group. Baseline demographics information can be found in Table 1, with p -values obtained from F and Chi-square tests. We plotted individual profiles modified by the cut-off scores and the mean profile evolution of biomarkers versus MMSE and ADAS-cog for males and females in Figure 1.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".