Effect of Single and Contiguous Teeth Extractions on Alveolar Bone Remodeling: A Study in Dogs
Bibliographic record
Abstract
BACKGROUND: Tooth extraction is associated with dimensional changes in the alveolar ridge. The aim was to examine the effect of single versus contiguous teeth extractions on the alveolar ridge remodeling. MATERIAL AND METHODS: Five female beagle dogs were randomly divided into three groups on the basis of location (anterior or posterior) and number of teeth extracted - exctraction socket classification: group 1 (one dog): single-tooth extraction; group 2 (two dogs): extraction of two teeth; and group 3 (two dogs): extraction of three teeth in four anterior sites and four posterior sites in both jaws. The dogs were sacrificed after 4 months. Sagittal sectioning of each extraction site was performed and evaluated using microcomputed tomography. RESULTS: Buccolingual or palatal bone loss was observed 4 months after extraction in all three groups. The mean of the alveolar ridge width loss in group 1 (single-tooth extraction) was significantly less than those in groups 2 and 3 (p < .001) (multiple teeth extraction). Three-teeth extraction (group 3) had significantly more alveolar bone loss than two-teeth extraction (group 2) (p < .001). The three-teeth extraction group in the upper and lower showed more obvious resorption on the palatal/lingual side especially in the lower group posterior locations. CONCLUSION: Contiguous teeth extraction caused significantly more alveolar ridge bone loss as compared with when a single tooth is extracted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".