Factors influencing the fracture of dental implants
Bibliographic record
Abstract
BACKGROUND: Implant fractures are rare but offer a challenging clinical situation. PURPOSE: To determine the prevalence of implant fracture and the possible risk factors predisposing an implant to a higher fracture risk. MATERIALS AND METHODS: This retrospective study is based on 2670 patients consecutively treated with implant-supported prostheses. Anatomical-, patient-, and implant-related factors were collected. Descriptive statistics and survival analyses were performed. Generalized estimating equations (GEE) evaluated the effect of explanatory variables on implant fracture. RESULTS: Forty-four implants (out of 10 099; 0.44%) fractured. The mean ± standard deviation time for fracture to occur was 95.1 ± 58.5 months (min-max, 3.8-294.7). Half of the occurrences of fracture happened between 2 and 8 years after implantation. Five factors had a statistically significant influence on the fracture of implants (increase/decrease in fracture probability): use of higher grades of titanium (decrease 72.9%), bruxism (increase 1819.5%), direct adjacency to cantilever (increase 247.6%), every 1 mm increase in implant length (increase 22.3%), every 1 mm increase in implant diameter (decrease 96.9%). CONCLUSIONS: It is suggested that 5 factors could influence the incidence of implant fractures: grade of titanium, implant diameter and length, cantilever, bruxism.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".