Bibliographic record
Abstract
Global advances in medicine, health, and nutrition are leading to a dramatically aging society. A 2001 U.N. report noted that 10 % of the world’s population today is over 60, and projected that this will increase to 20 % by 2050, and 33 % by 2150. As we age, we typically experience cognitive decline. Yet in many cases, disease results in even more serious and debilitating cognitive impairment. Degenerative disorders, which include cortical dementias such as Alzheimer’s disease (AD) and subcortical dementias such as Parkinson’s disease, are most prevalent. According to the Canadian Institutes of Health Research, AD currently affects nearly a quarter of million (238,000) Canadians; this number is projected to more than double, to nearly 500,000 by 2030. At the moment, caring for Canadians with AD costs about $5.5B each year. Worldwide incidence is projected to grow from a current level of 18 million to 34 million by 2025. More generally, the incidence of dementia worldwide may grow to 42 million by 2020, according to a report published in the Lancet in December. Cognitive impairments also result from a variety of other conditions that are not as prevalent — traumatic brain injuries; vascular disorders such as strokes; other progressive disorders of the central nervous system such as multiple sclerosis; toxic conditions such as alcoholism; infectious processes such as HIV and AIDS; brain tumors; oxygen deprivation; and metabolic conditions such as diabetes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.697 | 0.334 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".