Recent Progress in Alzheimer’s Disease Research, Part 3: Diagnosis and Treatment
Bibliographic record
Abstract
The field of Alzheimer's disease (AD) research has grown exponentially over the past few decades, especially since the isolation and identification of amyloid-β from postmortem examination of the brains of AD patients. Recently, the Journal of Alzheimer's Disease (JAD) put forth approximately 300 research reports which were deemed to be the most influential research reports in the field of AD since 2010. JAD readers were asked to vote on these most influential reports. In this 3-part review, we review the results of the 300 most influential AD research reports to provide JAD readers with a readily accessible, yet comprehensive review of the state of contemporary research. Notably, this multi-part review identifies the "hottest" fields of AD research providing guidance for both senior investigators as well as investigators new to the field on what is the most pressing fields within AD research. Part 1 of this review covers pathogenesis, both on a molecular and macro scale. Part 2 review genetics and epidemiology, and part 3 covers diagnosis and treatment. This part of the review, diagnosis and treatment, reviews the latest diagnostic criteria, biomarkers, imaging, and treatments in AD.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".