Bibliographic record
Abstract
Alzheimer's disease (AD) has increased from a few cases in a country at the beginning of the 20th century to an incidence of recording a case every 7 seconds in the world. From a rare disease it has reached the top 8 of major health problems in the world. One of the epidemiological problems of AD is the fact that authors from different countries use different reporting units. Some report numbers to 100,000 inhabitants, others to 1,000 inhabitants and others report the total number of cases in a country. Standardization of these reports is strictly necessary. The rise in incidence and prevalence with age is known, but interesting to see is that the incidence and prevalence do not rise in a parallel manner with age as simple logic would assume. Between the ages of 60 and 90, the incidence in men increases two times and in women 41 times, prevalence increase in men is 55.25-fold and in women 77-fold. Regarding the women/men ratio, the incidence is 20.5-fold increased, and prevalence is merely 1.3936-fold increased. These numbers raise concerns about the evolution of the disease. Regarding mild cognitive impairment (MCI)/AD ratio, only about 1 in 2 people get AD (raising?) issues about the pathogenic disease relatedness.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.006 | 0.003 |
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".