The role of oestrogen therapy in reducing risk of Alzheimer's disease: systematic review
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Bibliographic record
Abstract
BACKGROUND: Studies have shown a relationship between oestrogen and Alzheimer's disease. However, there is neither clear nor strong evidence on the use of oestrogen-only therapy in reducing the risk of Alzheimer's disease. AIMS: To assess the effects of oestrogen-only therapy on reducing the risk of Alzheimer's disease. METHOD: Inclusion criteria was determined with the PICO framework. Outcome was cognitive function measured by neuropsychological tests and strict protocols. Exclusion criteria included non-Alzheimer's dementia, progesterone-only therapy and pre-menopausal women. Searches were conducted in nine electronic healthcare databases, last searched in July 2022. Quality assessments conducted on randomised controlled trials (RCTs) were performed with the GRADE assessment, and cohort studies and case-control studies were assessed with the Newcastle-Ottawa Scale. Extracted data were used to analyse participants, interventions and outcomes. RESULTS: Twenty-four studies satisfied the search criteria (four RCTs, nine cohort studies, 11 case-control studies). Fifteen studies showed positive associations for oestrogen-only therapy reducing the risk of Alzheimer's disease, and the remaining nine found no evidence of association. CONCLUSIONS: Fifteen studies showed that oestrogen-only therapy effectively reduced the risk of Alzheimer's disease, whereas nine showed no correlation. Studies also investigated oestrogen-related variables such as length of oestrogen exposure, being an apolipoprotein E ε4 carrier and concomitant use of non-steroidal anti-inflammatory drugs, and their role in neuroprotection. This review was limited by the limited ranges of duration of oestrogen treatment and type of oestrogen-only therapy used. In conclusion, oestrogen-only therapy has potential for use in preventing Alzheimer's disease, although current evidence is inconclusive and requires further study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it