Bibliographic record
Abstract
Redressing the rising threat of dementia demands not only an increase, but a diversification of efforts. We need new approaches, trials, and partners. We cannot afford to continue to only round up the usual suspects, β amyloid, and tau and try to stop them with a single drug "silver bullet". Dementia of late onset is not a disease, but an amalgam of interactive pathologies on the shifting background of aging, requiring multimodal targeting. Cerebrovascular diseases coexist and coact with all major neurodegenerative pathologies, increasing two-fold the likelihood that they will manifest clinically. Cerebrovascular diseases need to be controlled, to give antidegenerative drugs a chance to succeed. This calls for new types of trials and designs. Stroke doubles the chances of developing dementia and decreases in stroke incidence correlate with decreases in dementia. Ninety percent of strokes are potentially preventable and so are a proportion of dementias. The stroke and dementia communities need to partner and complement the search for silver bullets with the golden opportunity of doing something now.
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 | 0.060 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".