Influence of Prosthetic Parameters on Peri‐Implant Bone Resorption in the First Year of Loading: A Multi‐Factorial Analysis
Bibliographic record
Abstract
BACKGROUND: The first year of prosthetic loading is crucial to peri-implant bone levels; however, contributing factors are yet barely understood. PURPOSE: The purpose of the study is to investigate the influence of patient-, implant-, and prosthetic-related parameters on marginal bone resorption in partially edentulous patients within the first year of prosthetic loading. MATERIALS AND METHODS: This retrospective multifactorial analysis involved the following influencing factors: patient gender and age, implant diameter, implant location and neck design, insertion torque, insertion depth, splinted versus single-tooth restorations, crown height space, and crown-to-implant ratio. RESULTS: Mean peri-implant bone resorption around 200 dental implants was 0.98 ± 0.76 mm and significantly correlated to higher implant insertion depth (p < .001), whereas no association to prosthetic parameters could be observed. CONCLUSIONS: Within the limits of the present analysis, it can be concluded that apical implant positioning may constitute a relevant determinant of early 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".