A novel quantitative approach to positron emission tomography for the diagnosis of Alzheimer’s disease
Bibliographic record
Abstract
The incidence of Alzheimer’s disease (AD) amongst the elderly in Canada (age >65) is expected to grow with increasing life expectancy. Current diagnostic methods are qualitative and yield equivocal results whose unreliability is exacerbated by variations in physician experience and technique. Therefore, there is a need for a quantitative method for interpreting Positron Emission Tomography (PET) brain scans. The method should be sensitive, specific, and capable of distinguishing between affected and unaffected individuals even in early disease stages. Here, scaled subprofile modeling/principal component analysis (SSM/PCA) and machine voting were used with 763 subjects from the Alzheimer’s disease Neuroimaging Initiative database and 99 subjects referred to the Health Sciences Centre – Winnipeg PET center between 2010 and 2012 to generate a machine voting score for Alzheimer’s disease (MVAD), which can distinguish between progressors and non-progressors from mild cognitive impairment to AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".