Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of age-related cognitive decline. Both active and passive immunization paradigms have illustrated the potential to prevent and reverse established AD pathology in transgenic and non-transgenic animal models of AD. Follow-up studies have shown that changes in amyloid burden observed with immunization could rescue cognitive deficits in both young and aged mice. Despite the success of immunotherapy in animal models, clinical trials were halted early. It has become clear that more preclinical work was needed before initiating trials, as most of the adverse events observed in patients could have been predicted using animal models. Despite these setbacks, clinical trials have demonstrated the utility of amyloid-beta (Abeta) vaccination in reducing amyloid pathology and potentially reducing cognitive decline. Several novel approaches to immunotherapy, including modified immunogens, adjuvants and modes of administration have been designed, which hold promise for human testing. Clinical trials using a safer vaccine, which is potent enough to elicit a robust antibody response in the absence of encephalitis may prove effective in mitigating progressive neurodegeneration seen in AD. If so, Abeta vaccination could supplant current symptomatic treatment and represent one of the first therapeutic options for AD based on the amyloid cascade hypothesis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".