Correlation of Implant Location to Marginal Bone Level Changes in Single‐Unit Restorations: A Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: The objective of the present study was to determine the emergence angle (EA) values and emergence profiles (EP) for single-unit fixed prosthetic restorations at the bone level, placed in different locations, and to evaluate their effect on radiographic marginal bone loss. METHODS: The study included 226 patients (mean age 63.61 ± 14.9 years), and 500 single-unit dental implants were analyzed in three implant localizations: molar, premolar, and anterior. Patient-related factors, implant length and diameter, implant brand, abutment retention type, implant placement time, prosthetic delivery loading type, prosthetic suprastructure type, and duration of prosthetic delivery time-from implant placement to long-term prosthetic functional loading-were recorded. Radiographically, EA, EP, marginal bone level changes (ΔMBL) at mesial and distal aspects were calculated. Receiver operating characteristic (ROC) curve analysis was employed to determine the cut-off point for all implant locations. Binary logistic regression analysis was utilized to identify confounding factors affecting MBL. RESULTS: The cut-off value of mesial EA was determined for molar, premolar, and anterior regions as 36.422°, 29.703°, and 25.12°, respectively. An increase in the duration of prosthetic delivery time, by every 1 month, the probability of MBL risk was 1.072 times higher. (OR: 1.072; CI: 1.009-1.139; p = 0.025) For the premolar localization variable, the OR value was determined as 2.381, and this indicated that the probability of bone loss in premolar implants is 2.381 times higher than in molar implants. Finally, the OR value of the anterior localization variable was obtained as 3.655, and this value indicated that the probability of bone loss in anterior implants is 3.655 times higher than in molar implants. CONCLUSIONS: The findings of this study indicate that ΔMBL can be evaluated over a range of EA values. It can be stated that as the duration of prosthetic delivery time increases following the surgical placement of dental implants, the risk of marginal bone loss also increases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".