AAIC draws nearly 12,000 researchers and health care professionals from around the globe to advance Alzheimer's and dementia science
Bibliographic record
Abstract
AAIC draws nearly 12,000 researchers and health care professionals from around the globe to advance Alzheimer's and dementia science Researchers and health care professionals from across the globe convened in Toronto in July to learn the latest in Alzheimer's and dementia diagnosis, treatment, and care at the 2025 Alzheimer's Association International Conference ® (AAIC ® ).With more than 8000 attendees joining in person and more than 3000 joining remotely, AAIC offered a choice of 148 scientific sessions, 737 podium presentations, and more than 5000 poster presentations from which attendees could advance their knowledge.Participants represented 139 countries, and 41% were first-time AAIC attendees.Topics included the biological underpinnings of disease, learnings in recruitment and care science, diversification of the clinical trial pipeline, treatment updates, advances in tools for detection and diagnosis, and risk factors across the lifespan.In addition, on the last day of the conference more than 8000 individuals participated in AAIC For All, a no-cost hybrid event providing key takeaways from the conference to both the general public and health care professionals.Following are some of the key stories reported at AAIC 2025. Lifestyle interventions U.S. POINTERResearch suggests that maintaining overall physical health may also promote brain health.Lifestyle interventions, including physical activ-
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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.197 | 0.138 |
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".