Retrospective evaluation of marginal bone loss around implants in a mandibular locator‐retained denture using panoramic radiographic images and finite element analysis: A pilot study
Bibliographic record
Abstract
BACKGROUND: The follow-up of the peri-implant marginal bone loss is the most important criterion for the determination of implant success. PURPOSE: The purpose of this study is to measure marginal bone loss using panoramic radiographic images (PRI) of patients treated using a mandibular, two implant-supported, locator-retained denture and to evaluate the compatibility of these findings with those of the finite element analysis (FEA). MATERIALS AND METHODS: The PRI of patients who had a mandibular, two implant-supported, locator-retained denture were assessed, and the mesial and distal marginal bone loss of both right- and left-sided implants was measured. Mandibular and maxillary models, which have the features of bilateral balanced occlusion, were created. The surfaces of the generated models were converted in a computer-aided design model that could be transferred to the FEA software, and the forces were defined on contacts formed in maximum intercuspation, lateral, and protrusive movement position for bilateral balanced occlusion. RESULTS: The bone loss in the mesial and distal regions at the right- and left-sided implants was not statistically significant. Higher stresses were formed on the vestibular side under protrusive movement, on the lingual side under maximum intercuspation, on the distolingual side under left unilateral biting, and on the mesiolingual side under right unilateral biting in the FEA. CONCLUSION: According to FEA, peri-implant bone resorption may be higher in the buccal and palatal regions, implying that panoramic radiographs can be misleading in understanding the amount of peri-implant bone resorption.
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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) | 0.000 | 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) | 0.001 | 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".