BUCCAL BONE THICKNESS IN RETROMOLAR AREA IN RELATION TO BONE GRAFTING PRIOR TO DENTAL IMPLANTS. A CONE BEAM COMPUTED TOMOGRAPHY STUDY
Bibliographic record
Abstract
Objective: To investigate the buccal bone thickness lateral to Inferior alveolar nerve at mandibular retromolar area using cone beam computed tomography (CBCT) scans. Materials and Methods: CBCT records of patients attending department of oral and maxillofacial surgery, University College of Dentistry, University College of Lahore from January 2018 till April 2020 were included in the present study. CBCT software was used to generate slices of 10mm in width starting from the cementoenamel junction (CEJ) on the distal side of the lower second molar in the panoramic view. The sagittal cross section at this point was used to measure the shortest distance from inferior alveolar nerve (IAN) to the outer cortical plate of mandible on both sides of mandible. t- test was used compare the mean bone thickness. Results: There was no significant difference in mean bone thickness between the right and left side of mandible in both the genders (P>0.05). However, mean bone thickness varied significantly on right (P=0.012) and left side (P=0.019) of mandible between males and females. Conclusion: Retromolar area is a convenient source of autogenous bone as there is adequate thickness of bone in males and female however, care should be taken in females when retromolar area is chosen as a donor site. The role of CBCT in preoperative planning is crucial in determining the exact dimensions of the buccal bone thickness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".