Incidence and pattern of implant fractures: A long‐term follow‐up multicenter study
Bibliographic record
Abstract
BACKGROUND: Currently, there is incomplete understanding of the fracture patterns in the implant and their association with clinical factors. PURPOSE: The aim of this study was to investigate the incidence and pattern of implant fracture (IF) by using 9-year, long-term multicenter follow-up data. MATERIALS AND METHODS: The association of the incidence and differences in fracture patterns with clinical factors (based on patient variables and implant variables) was assessed for statistical significance using the Chi-square and Fisher exact test, as appropriate. RESULTS: Among a total of 19 087 implants in 8501 patients (7838 male and 663 female) placed over 9 years, fractures were observed in 70 implants (0.4%) in 57 patients (50 male and 7 female). Cases with less than 50% bone loss had a higher incidence of horizontal and vertical IFs limited to the crest module, which are defined as Type I fractures (n = 13, 18.6%). In contrast, cases with ≥50% severe bone loss exhibited a higher incidence of Type II vertical fractures (n = 22, 31.4%), extending beyond the crestal portion (P = .001). Type III fractures (n = 5, 7.1%), defined as a horizontal fracture beyond the crestal module, were also observed. CONCLUSION: Peri-implantitis-induced marginal and vertical bone loss and manufacturing-induced defects were considered to be major factors in IF. Therefore, using clinically verified implant systems and striving to minimize bone loss by preventing and actively treating peri-implantitis is essential to reduce IFs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".