Clinical assessment of the palatal alveolar bone thickness and its correlation with the buccolingual angulation of maxillary incisors for immediate implant placement
Bibliographic record
Abstract
BACKGROUND: Clinicians needed to be aware of the thinness of the palatal bone plate when identifying severe protrusion of maxillary incisors, as this could result in perforation of the palatal bone plate during surgical procedures associated with immediate maxillary implant placement. PURPOSE: This study evaluated the thickness of the palatal bone plate, the buccolingual angulation of maxillary incisors, and the correlation between these two parameters in relation to immediate implant placement. MATERIALS AND METHODS: Cone-beam computed tomography images of 37 Asian patients (20-60 years old) with no evidence of dental treatment in the maxillary incisor area were collected and classified into groups based on sex and incisor locations. Sagittal slices were used to measure the palatal bone plate thickness at the cervical, middle, and apical levels, and the buccolingual angulation of each tooth was also measured. The intraclass correlation, Shapiro-Wilk test, descriptive analysis, Student's t-test, and Pearson correlation were used for statistical and correlation analyses, with P < .05 applied as the criterion for statistical significance. RESULTS: At the apical level of all inspected teeth and the middle level of lateral incisors, the palatal bone was significantly thicker in males than in females. The inclination of the maxillary lateral incisor showed a moderate negative linear correlation with the palatal bone thickness at the apical level (R = -0.517 and R = -0.579 for males and females, respectively). CONCLUSIONS: In an Asian population, an increased buccolingual angulation of the maxillary lateral incisors was correlated with a thinner palatal bone plate at the apical level.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".