Acetylcholinesterase inhibitors and the risk of hip fracture in Alzheimer's disease patients: A case-control study
Bibliographic record
Abstract
Recent studies have reported the presence of acetylcholine (ACh) receptor subtypes in bone tissue, and have demonstrated that inhibition of the ACh receptors has negative effects on bone mass and fracture healing capacity. However, little is known about the potential clinical effects that increased ACh signaling might have on bone. Accordingly, this study was designed to determine whether the use of acetylcholinesterase inhibitors (AChEIs), a group of drugs that stimulate ACh receptors and are used to treat Alzheimer's disease (AD), is associated with a decreased risk of hip fracture in AD patients. To accomplish this objective, a case-control analysis was performed using the AD population, aged above 75 years, based in the local health area of the Carlos Haya Hospital, in Malaga, Spain. The cases were 80 AD patients that suffered a hip fracture between January 2004 and December 2008. The controls were 2178 AD patients without hip fracture followed at our health care area during the same period of time. Compared with patients who did not use AChEIs, the hip fracture adjusted odds ratio (OR) for users of AChEIs was 0.42 (95% confidence interval [CI], 0.24-0.72), for users of rivastigmine was 0.22 (95% CI, 0.10-0.45), and for users of donepezil was 0.39 (95% CI, 0.19-0.76). Data were adjusted for the following parameters: body mass index, fall risk, smoking habits, cognition, dependence, degree of AD, comorbidity score, treatment with selective serotonin reuptake inhibitors, age, and gender. Our data suggests that use of AChEIs donepezil and rivastigmine is associated with a reduced risk of fractures in AD patients. Many elderly patients with AD disease who are at risk of developing osteoporosis may potentially benefit from therapy with the AChEIs donepezil and rivastigmine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".