High-Frequency Acceleration
Bibliographic record
Abstract
A common problem in clinical dentistry is the significant and rapid bone loss that occurs after tooth extraction. Currently there is no solution for the long-term preservation of alveolar bone. Previously, we showed that high-frequency acceleration (HFA) has an osteogenic effect on healthy alveolar bone. However, it is not known if HFA can preserve alveolar bone after extraction without negatively affecting wound healing. The purpose of this study was to evaluate the effect of HFA on alveolar bone loss and the rate of bone formation after tooth extraction. Eighty-five adult Sprague-Dawley rats were divided into 3 groups: control, static (static load), and HFA. In all groups, the maxillary right third molar was extracted. The HFA group received HFA for 5 min/d, applied through the second molar. The static group received the same magnitude of static load. The control group did not receive any stimulation. Some animals received fluorescent dyes at 26 and 54 d. Samples were collected on days 0, 7, 14, 28, and 56 for fluorescence microscopy, micro-computed tomography, histology, RNA, and protein analyses. We found that HFA increased bone volume in the extraction site and surrounding alveolar bone by 44% when compared with static, while fully preserving alveolar bone height and width long-term. These effects were accompanied by increased expression of osteogenic markers and intramembranous bone formation and by decreased expression of osteoclastic markers and bone resorption activity, as well as decreased expression of many inflammatory markers. HFA is a noninvasive safe treatment that can be used to prevent alveolar bone loss and/or accelerate bone healing after tooth extraction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".