Combining hippocampal volume metrics to better understand Alzheimer’s disease progression in at-risk individuals
Bibliographic record
Abstract
To date nearly all clinical trials of Alzheimer's disease (AD) therapies have failed. These failures are, at least in part, attributable to poor endpoint choice and to inadequate recruitment criteria. Recently, focus has shifted to targeting at-risk populations in the preclinical stages of AD thus improved predictive markers for identifying individuals likely to progress to AD are crucial to help inform the sample of individuals to be recruited into clinical trials. We focus on hippocampal volume (HV) and assess the added benefit of combining HV and rate of hippocampal atrophy over time in relation to disease progression. Following the cross-validation of previously published estimates of the predictive value of HV, we consider a series of combinations of HV metrics and show that a combination of HV and rate of hippocampal atrophy characterises disease progression better than either measure individually. Furthermore, we demonstrate that the risk of disease progression associated with HV metrics does not differ significantly between clinical states. HV and rate of hippocampal atrophy should therefore be used in tandem when describing AD progression in at-risk individuals. Analyses also suggest that the effects of HV metrics are constant across the continuum of the early stages of the disease.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".