Alveolar ridge dimensional changes following ridge preservation procedure using<scp>S</scp>ocket<scp>KAP</scp><sup>™</sup>: exploratory study of serial cone‐beam computed tomography and histologic analysis in canine model
Bibliographic record
Abstract
INTRODUCTION: The aim of this pilot study was to examine the kinetics of alterations in alveolar ridge width and height following tooth extraction with and without ridge preservation, using anorganic bovine bone mineral (ABBM) and a novel device (SocketKAP(™) ) designed for obturation of socket orifice. MATERIALS AND METHODS: Maxillary left and right PM1 and mandibular right PM2 and PM4 were extracted on six beagle dogs and treated as follows: Group A: negative control; Group B: SocketKAP(™) alone; Group C: ABBM + SocketKAP(™) . Serial cone-beam computed tomography (CBCT) was taken at 0-, 1-, 2-, 4-, 8- and 12-week intervals to calculate the rate of alveolar bone loss, followed by histologic and histomorphometric analyses at 12 weeks. Across group outcomes were compared. RESULTS: Without additional intervention, the crestal-most 3 mm of alveolar bone width lost approximately 0.21-0.28 mm per week. The rate of alveolar buccal bone height loss was 0.19 mm per week. Comparatively, in group C, the alveolar bone was relatively stable, with loss of only 0.003-0.13 mm of width and 0.12 mm of height per week. These differences were statistically significant. The alveolar bone in sites treated by SocketKAP(™) alone was significantly different from control only at select time points and locations of the ridge, presumably due to small sample size. CONCLUSION: Without additional intervention, tooth extraction was accompanied by rapid loss of alveolar ridge width and height. Applications of SocketKAP(™) and ABBM were effective in reducing alveolar crestal width and height loss following tooth extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".