Polymorphisms in Inflammatory Genes and the Risk of Alzheimer Disease
Bibliographic record
Abstract
The concept of inflammation as a major factor in Alzheimer disease (AD) has heretofore been based on postmortem findings of autodestructive changes associated with the lesions coupled with epidemiological evidence of a protective effect of anti-inflammatory agents. Now there is evidence that the risk of AD is substantially influenced by a total of 10 polymorphisms in the inflammatory agents interleukin 1alpha, interleukin 1beta, interleukin 6, tumor necrosis factor alpha, alpha(2)-macroglobulin, and alpha(1)-antichymotrypsin. The polymorphisms are all common ones in the general population, so there is a strong likelihood that any given individual will inherit 1 or more of the high-risk alleles. The overall chances of an individual developing AD might be profoundly affected by a "susceptibility profile" reflecting the combined influence of inheriting multiple high-risk alleles. Since some of the polymorphisms in question have already been linked to peripheral inflammatory disorders, such as juvenile rheumatoid arthritis, myasthenia gravis, and periodontitis, associations between AD and several chronic degenerative diseases may eventually be demonstrated. Such information could lead to strategies for therapeutic intervention in the early stages of such disorders.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".