P2‐245: Dynamic biomarker model in Alzheimer's disease: Longitudinal analysis of hippocampal volume shows linear decline
Bibliographic record
Abstract
Statistical analysis of longitudinal biomarker data is necessary to prove or disprove hypotheses regarding Alzheimer's disease (AD) biomarker trajectories in preclinical to clinical disease stages. The most prevalent hypothesis, as proposed in Jack et al. (Lancet Neurol., 2010), is for a sigmoidal relationship between biomarkers and disease severity. Preliminary evidence from models based on baseline ADNI data (Caroli et al., Neurobiol. Aging, 2010; Mouiha et al., J. Alz. Dis., 2012) have not confirmed this hypothesis for major AD biomarkers. The objective of this study was to investigate the longitudinal shape of this association between a well-known structural biomarker of AD and disease severity. We selected 135 mild cognitive impairment (MCI) subjects from the ADNI dataset (49 females, 86 males) who converted to probable AD within 36 months. Left and right h ippocampal volumes (HC) were measured using FreeSurfer software at every six months from 1.5T v olumetric MP-RAGE MR scans. For modeling purposes we used the average of left and right HC volumes. An analysis for repeated measures was used to compare volumes and MMSE between time points. All subjects were ordered based on their score on the Mini-Mental State Examination (MMSE) as a surrogate marker of time. To determine HC volumes variations with MMSE, we initialized the time of progression from MCI to AD when MMSE score fell between 21–24. We plotted individual profiles and calculated regressions in 6-months intervals before and after progression. By estimating individual regression models and calculating the means of the estimated parameters, we obtained a mean regression profile. Summary biomarker information can be found in Table 1. Figure 1 shows individual HC volume profiles against time to progression, while Figure 2 shows individual and mean regression profiles based on this data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".