CBCT Data Relevant in Treatment Planning for Immediate Mandibular Molar Implant Placement
Bibliographic record
Abstract
Background: Immediate molar implants (IMIs) have been shown to provide an effective treatment, but their placement comes with potential anatomically related risks. Methods: CBCTs of>400 dental sites were analyzed for key anatomical features at mandibular molar sites that can impact the placement of IMIs. Features measured included distances from each molar furcation to points risking lingual plate perforation or inferior alveolar nerve (IAC) damage, distances from molar root apices to IAC, mesiodistal and buccolingual widths of molar inter-septal bone (ISB), and thicknesses of buccal and lingual cortical plates at first and second mandibular molar sites. Results: Distances from molar furcations to contact with lingual cortical plates and to IAC decreased significantly from mesial to distal, as did distances from root apices to the mandibular canal. Both buccolingual and mesiodistal ISB widths and thicknesses of buccal and lingual cortical plates increased mesiodistally. Buccolingual ISB widths were largest coronally for both molar sites and decreased apically. The reverse was found with mesiodistal septal ISB widths, which increased coronoapically. Conclusion: Risks of lingual perforations or IAC damage were significantly greater at second molars vs. first molars. The ability to place IMIs in ISB at first molars was estimated to be>twice as often as at second molars. Maximal implant lengths for IMIs placed in the furcal bone should not exceed 10 mm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".