Influence of age, disease onset and <i>ApoE4</i> on visual medial temporal lobe atrophy cut‐offs
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Bibliographic record
Abstract
BACKGROUND: Visual assessment of medial temporal lobe atrophy (MTA; range 0-4, from no atrophy to increasing atrophy of the choroid fissure, temporal horns and hippocampus) is a sensitive radiological marker of Alzheimer's disease (AD). One of the critical elements for visual MTA assessment is the cut-off score that determines deviation from normality. METHODS: In this study, we assessed the sensitivity and specificity of different MTA cut-off scores to classify control subjects, individuals with mild cognitive impairment (MCI) and AD patients from two large independent cohorts, AddNeuroMed and Alzheimer's Disease Neuroimaging Initiative. Of note, we evaluated the effects of clinical, demographic and genetic variables on the classification performance according to the different cut-offs. RESULTS: A cut-off of ≥1.5 based on the mean MTA scores of both hemispheres showed higher sensitivity in classifying patients with AD (84.5%) and MCI subjects (75.8%) who converted to dementia compared to an age-dependent cut-off. The age-dependent cut-off showed higher specificity or ability to correctly identify control subjects (83.2%) and those with MCI who remained stable (65.5%). Increasing age, early-onset disease and absence of the ApoE ε4 allele had a stronger influence on classifications using the ≥1.5 cut-off. Above 75 years of age, an alternative cut-off of ≥2.0 should be applied to achieve a classification accuracy for both patients with AD and control subjects that is clinically useful. CONCLUSION: Clinical, demographic and genetic variables can influence the classification of MTA cut-off scores, leading to misdiagnosis in some cases. These variables, in addition to the differential sensitivity and specificity of each cut-off, should be carefully considered when performing visual MTA assessment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it