Diagnosis and treatment of dementia: Overview
Bibliographic record
Abstract
The development of accurate diagnostic tests and treatment of dementia must be important issues in an aging society. The quality of biomarkers for dementia have dramatically improved recently and are classified into two categories, including (i) biochemical markers in biofluids and (ii) imaging using radiological technologies. Positron emission tomography (PET) to detect amyloid β was first developed in 2004 (1)). Since then, several amyloid PET tracers to detect senile plaques in patients with Alzheimer's disease (AD) have been published by many investigators, including our group (2)). Some laboratories recently developed PET tracers to detect tau pathologies in patients with AD (3)). Moreover, four drugs (donepezil, galantamine, rivastigmine, and memantin), which modulate neurotransmission in the brains of patients with AD are now used to treat AD; however, none of them can cure the disease. Although several anti-amyloid β compounds have been examined in clinical trials as potentially useful drugs, all of them have failed to show significant benefits so far. In contrast, tau-targeted drugs have been developed and have entered clinical trials. We expect strongly a therapeutic drug for dementia to be released in the near future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".