Diagnostic accuracy of markers for prodromal Alzheimer's disease in independent clinical series
Bibliographic record
Abstract
OBJECTIVE: To capitalize on data from different clinical series to compare sensitivity and specificity of individual biomarkers for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD). METHODS: Medial temporal atrophy, cortical hypometabolism, and cerebrospinal fluid biomarkers were assessed in 18 patients with mild cognitive impairment (MCI) with prodromal AD (pAD; conversion time, 26 ± 12 months) and 18 stable MCI (sMCI) patients from the Translational Outpatient Memory Clinic cohort, as well as in 24 pAD patients (conversion time, 36 ± 12 months) and 33 sMCI patients from the Alzheimer's Disease Neuroimaging Initiative cohort. Medial temporal atrophy was measured by manual, semi-automated, and automated hippocampal volumetry; cortical hypometabolism was measured using several indices of AD-related hypometabolism pattern; and cerebrospinal fluid markers were amyloid β (Aβ)42 and total tau protein concentrations. For each biomarker, sensitivity for pAD, specificity for sMCI, and diagnostic accuracy were computed. RESULTS: Sensitivity to predict MCI conversion to AD in the Alzheimer's Disease Neuroimaging Initiative and Translational Outpatient Memory Clinic cohorts was 79% and 94% based on Aβ42, 46% and 28% based on hippocampal volumes, 33% to 66% and 56% to 78% based on different hypometabolism indices, and 46% and 61% based on total tau levels, respectively. Specificity to exclude sMCI was 27% and 50% based on Aβ42, 76% and 94% based on hippocampal volumes, 58% to 67% and 55% to 83% based on different hypometabolism indices, and 61% and 83% based on total tau levels, respectively. CONCLUSIONS: Current findings suggest that Aβ42 concentrations and hippocampal volumes may be used in combination to best identify pAD.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".