200 Canadian Family Physician • Le Médecin de famille canadien d VOL 52: FEBRUARY • FÉVRIER 2006 CME Prevention of Alzheimer disease Encouraging evidence
Bibliographic record
Abstract
OBJECTIVE To review the evidence regarding prevention of Alzheimer disease (AD) in order to highlight the role of family medicine. QUALITY OF EVIDENCE Most of the evidence relating to prevention of AD is derived from observational (cross-sectional, case-control, or longitudinal) studies. Evidence from randomized controlled trials (RCTs) is available only for blood pressure control and for hormone replacement therapy for menopausal women. MAIN MESSAGE Many preventive approaches to AD have been identifi ed, but no RCTs support their effi cacy. Evidence from RCTs supports the eff ectiveness of blood pressure control in reducing incidence of AD, but demonstrates that postmenopausal women’s use of estrogen is ineff ective in reducing it. Observational studies suggest that some preventive approaches, such as healthy lifestyle, ongoing education, regular physical activity, and cholesterol control, play a role in prevention of AD. These approaches can and should be used for every patient because they carry no signifi cant risk. Currently, no eff ective pharmacologic interventions have been researched enough to support their use in prevention of AD. CONCLUSION Health professionals should educate patients, especially patients at higher risk of AD, about preventive strategies and potentially modifi able risk factors.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 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".