Risk assessment of labial bone perforation in the anterior mandibular region: a virtual immediate implant placement study
Bibliographic record
Abstract
BACKGROUND: This study investigated the prevalence of labial bone perforation (LBP) related to the associated anatomic factors in anterior mandibular region using a virtual immediate implant placement procedure. METHODS: Series qualified CBCT images of 149 participants (894 teeth) were selected to analyze the assigned anatomical parameters, including concavity depth, concavity angle, torque, and deep bone thickness. Four classes of crestal and radicular dentoalveolar bone phenotypes (CRDAPs) of mandibular anterior teeth were categorized according to the thickness of dentoalveolar bone at both crestal and radicular zones. Data were adjusted for categorical (gender and CRDAP) and continuous (age, cavity angle, cavity depth, and deep bone thickness) variables using a multivariable logistic regression analysis with generalized estimating equation method. RESULTS: The overall probability of LBP after virtual implant placement was 21.6%. There is statistically significant higher prevalence of LBP at canine (28.5%) and CRDAP class II (29.2%) regions (p < 0.001). After adjusting confounding variables, CRDAP class II and class IV regions are more likely to have LBP when compared with CRDAP class I (control) regions (p < 0.01). The risk of LBP at canine site is 6.31 times more likely than at the central incisor (control) (p < 0.01). CONCLUSIONS: Using a virtual immediate implant placement technique, the prevalence of LBP is significantly higher at the mandibular canine site and thin radicular dentoalveolar phenotype in the anterior mandibular region.
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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 | 0.002 |
| 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 | 0.001 |
| 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".