Factors Affecting Late Fixture Loss and Marginal Bone Loss Around Teeth and Dental Implants
Bibliographic record
Abstract
BACKGROUND: The predictability and high success rate of implant treatment have averted attention from factors affecting fixture loss and bone loss around implants. PURPOSE: The goal of this study was to retrospectively evaluate late fixture loss and marginal bone loss around implants that have been in function for 5 years and to relate these findings to bone loss in the natural dentition. MATERIALS AND METHODS: One hundred and forty-three consecutively treated patients who had received an implant-anchored fixed prosthesis and completed a 5-year follow-up were selected. Intraoral and panoramic radiographs were used to assess bone loss. RESULTS: The bone loss was greater around remaining implants in patients who had lost implants after loading. No correlation was found between bone loss around implants and that around teeth. Only 2% of the fixtures were lost during 5 years of functional load. Most fixtures losses occurred in the edentulous maxilla. Seven of the nine patients who lost fixtures were smokers. CONCLUSION: These findings show that patients who lost implants also lost more bone around the remaining implants. There was no correlation between bone loss around implants and that around teeth, indicating that different interacting mechanisms are involved.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".