Characterization of a temporoparietal junction subtype of Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) subtypes have been described according to genetics, neuropsychology, neuropathology, and neuroimaging. Thirty-one patients with clinically probable AD were selected based on perisylvian metabolic decrease on FDG-PET. They were compared to 25 patients with a typical pattern of decreased posterior metabolism. Tree-based machine learning was used on those 56 images to create a classifier that was subsequently applied to 207 Alzheimer's Disease Neuroimaging Initiative (ADNI) patients with AD. Machine learning was also used to discriminate between the two ADNI groups based on neuropsychological scores. Compared to AD patients with a typical precuneus metabolic decrease, the new subtype showed stronger hypometabolism in the temporoparietal junction. The classifier was able to distinguish the two groups in the ADNI population. Both groups could only be distinguished cognitively by Trail Making Test-A scores. This study further confirms that there is more than a typical metabolic pattern in probable AD with amnestic presentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".