Bibliographic record
Abstract
We propose a single, quantitative metric called the disease evaluation factor (DEF) and assess its efficiency at estimating disease burden in normal, control subjects (CTRL) and probable Alzheimer's disease (AD) patients. The study group consisted in 75 patients with a diagnosis of probable AD and 75 age-matched normal CTRL without neurological or neuropsychological deficit. We calculated a reference eigenspace of MRI appearance from reference data, in which our CTRL and probable AD subjects were projected. We then calculated the multi-dimensional hyperplane separating the CTRL and probable AD groups. The DEF was estimated via a multidimensional weighted distance of eigencoordinates for a given subject and the CTRL group mean, along salient principal components forming the separating hyperplane. We used quantile plots, Kolmogorov-Smirnov and χ2 tests to compare the DEF values and test that their distribution was normal. We used a linear discriminant test to separate CTRL from probable AD based on the DEF factor, and reached an accuracy of 87%. A quantitative biomarker in AD would act as an important surrogate marker of disease status and progression.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".