Bibliographic record
Abstract
Alzheimer's disease (AD) has become more and more common in older people, and the data show that the number of people with AD might increase rapidly in the next few decades if there are no interventions and prevention. However, some people are still confused between AD and dementia. Lots of people believe that dementia is a sign of getting old, so it is common and normal. Thus, some people with AD may miss the most important time to find treatment, which could cause them to be in a more serious condition. Knowledge about AD is still not propagated. This paper wants to show the importance of familiarity with the symptoms and treatment of AD. Forgetfulness is one of the typical symptoms, but there are other symptoms that people are not aware of, such as personality change, depression, and language ability. Therefore, people need to either prevent this disease or get intervention earlier. Although there is no medication or surgery to cure AD, nonpharmacologic and pharmacologic treatments are helpful to reduce some symptoms and slow the progress of AD. A healthy lifestyle also plays a significant role in preventing AD, and a healthy lifestyle could decrease the risk of AD and have positive outcomes for people with 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 | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".