Mathematical model of the Alzheimer’s disease biomarker cascade demonstrates statistical pitfall in identifying surrogates of cognitive reserve
Bibliographic record
Abstract
Statistical interaction analyses with biomarkers of pathology on cognitive outcome have been put forward to investigate neurobiological surrogates of cognitive reserve in Alzheimer's disease (AD). However, as these potential surrogates are likely affected by neurodegeneration during the course of AD, their joint alteration with biomarkers of pathology and cognitive outcome during disease progression may introduce misinterpretable interaction effects in cross-sectional studies. To demonstrate this, we conducted interaction analyses on synthetic data from a mathematical model of the AD biomarker cascade. When randomly sampling cross-sectionally, these gave interaction effects, which greatly reduced when controlling for the corresponding time point of each sampled data point. Cross-sectional studies investigating cognitive reserve using interaction analyses are advised to take into account the different time stages of the disease that individual data points represent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".