Effect of non-steroidal anti-inflammatory drugs on risk of Alzheimer's disease: systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVES: To quantify the risk of Alzheimer's disease in users of all non-steroidal anti-inflammatory drugs (NSAIDs) and users of aspirin and to determine any influence of duration of use. DESIGN: Systematic review and meta-analysis of observational studies published between 1966 and October 2002 that examined the role of NSAID use in preventing Alzheimer's disease. Studies identified through Medline, Embase, International Pharmaceutical Abstracts, and the Cochrane Library. RESULTS: Nine studies looked at all NSAIDs in adults aged > 55 years. Six were cohort studies (total of 13 211 participants), and three were case-control studies (1443 participants). The pooled relative risk of Alzheimer's disease among users of NSAIDs was 0.72 (95% confidence interval 0.56 to 0.94). The risk was 0.95 (0.70 to 1.29) among short term users (< 1 month) and 0.83 (0.65 to 1.06) and 0.27 (0.13 to 0.58) among intermediate term (mostly < 24 months) and long term (mostly > 24 months) users, respectively. The pooled relative risk in the eight studies of aspirin users was 0.87 (0.70 to 1.07). CONCLUSIONS: NSAIDs offer some protection against the development of Alzheimer's disease. The appropriate dosage and duration of drug use and the ratios of risk to benefit are still unclear.
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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".