Postoperative Administration of the Acetylcholinesterase Inhibitor, Donepezil, Interferes with Bone Healing and Implant Osseointegration in a Rat Model
Bibliographic record
Abstract
Donepezil is an acetylcholinesterase inhibitor commonly used to treat mild to moderate Alzheimer's disease. Its use has been associated with increased bone mass in humans and animals. However, the effect of postoperative administration of donepezil on bone healing remains unknown. Therefore, this study aimed to assess the impact of postoperative injection of donepezil on bone healing, titanium-implant osseointegration, and soft tissue healing. Twenty-two Sprague-Dawley rats were randomly assigned to receive a daily dose of either donepezil (0.6 mg/kg) or saline as a control. In each rat, a uni-cortical defect was created in the right tibia metaphysis and a custom-made titanium implant was placed in the left tibiae. After two weeks, rats were euthanized, and their bones were analysed by Micro-CT and histology. The healing of bone defect and implant osseointegration in the rats treated with donepezil were significantly reduced compared to the saline-treated rats. Histomorphometric analysis showed lower immune cell infiltration in bone defects treated with donepezil compared to the saline-treated defects. On the other hand, the healing time of soft tissue wounds was significantly shorter in donepezil-treated rats compared to the controls. In conclusion, short-term administration of donepezil hinders bone healing whereas enhancing soft tissue healing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".