[no title]
Bibliographic record
Abstract
Biomarkers play an important role in the study of neurological disease. In neurodegenerative disease, the pathophysiologic process leading to neuron death begins before clinical symptoms develop. Therefore, one of the most important roles of biomarkers is an accurate diagnosis of diseases in their early and presymptomatic stages. Another important role of biomarkers is to serve as potential surrogate markers of disease severity. Biomarkers can also be used to improve safety assessment and determine appropriate dosage of the drug. Various biomarkers have been developed for clinical assessment of Alzheimer's disease. Tau and amyloid-β protein in cerebrospinal fluid are useful biomarkers for early diagnosis of Alzheimer's disease. Recent development of molecular imaging probes enables noninvasive detection of amyloid plaques using positron emission tomography (PET). PET amyloid imaging may be useful for early and accurate diagnosis of Alzheimer's disease, patient selection for disease-modifying therapeutic trials, and monitoring of the effect of anti-amyloid therapy. A multisite, prospective clinical study was launched to develop standardized neuroimaging and biomarker methods for clinical trial on Alzheimer's disease.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.054 |
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".