Bibliographic record
Abstract
Aging occurs as a series of small steps, first causing cellular damage and then affecting tissues and organs. This is also true in the brain. Frailty, a state of increased risk due to accelerated deficit accumulation, is robustly a risk factor for cognitive impairment. Community-based autopsy studies show that frail individuals have brains that show multiple deficits without necessarily demonstrating cognitive impairment. These facts cast a new light on the growing number of risk factors for cognitive impairment, suggesting that, on a population basis, most health deficits can be associated with late-life cognitive impairment. The systems mechanism by which things that are bad for the body are likely to be bad for the brain can be understood like this: the burden of health deficits anywhere indicates impaired ability to withstand or repair endogenous and environmental damage. This in turn makes additional damage more likely. If true, this suggests that a life course approach to preventing cognitive impairment is desirable. Furthermore, conducting studies in highly selected, younger, healthier individuals to provide 'proof of concept' information is now common. This strategy might exclude the very circumstances that are required for disease expression in the people in whom dementia chiefly occurs (that is, older adults who are often in poor health).
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".