Pressure Bearing Device Affects Extraction Socket Remodeling of Maxillary Anterior Tooth. A Prospective Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Extraction socket remodeling and ridge preservation strategies have been extensively explored. PURPOSE: To evaluate the efficacy of applying a micro-titanium stent as a pressure bearing device on extraction socket remodeling of maxillary anterior tooth. MATERIALS AND METHODS: Twenty-four patients with a extraction socket of maxillary incisor were treated with spontaneous healing (control group) or by applying a micro-titanium stent as a facial pressure bearing device over the facial bone wall (test group). Two virtual models obtained from cone beam computed tomography data before extraction and 4 months after healing were 3-dimenionally superimposed. Facial bone wall resorption, extraction socket remodeling features and ridge width preservation rate were determined and compared between the groups. RESULTS: Thin facial bone wall resulted in marked resorption in both groups. The greatest palatal shifting distance of facial bone located at the coronal level in the control group, but middle level in the test group. Compared with the original extraction socket, 87.61 ± 5.88% ridge width was preserved in the test group and 55.09 ± 14.46% in the control group. CONCLUSIONS: Due to the facial pressure bearing property, the rigid micro-titanium stent might preserve the ridge width and alter the resorption features of extraction socket.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".