Analysis of Risk Factors for Early Implant Failure in the Anterior Region: A Retrospective Study of 2620 Implants
Bibliographic record
Abstract
OBJECTIVES: This study aimed to identify risk factors associated with early implant failure in the anterior maxillary and mandibular regions. MATERIALS AND METHODS: A total of 2023 patients with 2620 implants placed in the maxillary and mandibular anterior regions between January 2020 and June 2023 were included in this study. Clinical and radiographic data were extracted from medical records and imaging software. In organizing the information, 19 variables were categorized into patient-related factors (gender, age, periodontitis, reasons for tooth loss, bone quality, and penicillin allergy), implant-related factors (implant system, bone level/soft tissue level, diameter, and length), and surgical factors (jaw position, placement timing, bone grafting, bone compression/splitting surgery, concentrated growth factors (CGFs), bone graft materials, barrier membrane, torque, and healing style). Univariate and multivariate Cox proportional hazards regression models were used to identify significant risk factors for early failure. RESULTS: The cumulative survival rate (CSR) of all implants after a 0- to 43-month observation period was 95.6% (95% confidence interval [CI]: 94.8%-96.4%). Independent risk factors for early implant failure included non-submerged healing (hazard ratio [HR] = 3.000, 95% CI = 1.712-5.256), torque < 30 N/cm (HR = 13.193, 95% CI = 8.439-20.626), and Type I bone quality (HR = 3.220, 95% CI = 1.413-7.342) (all p < 0.05). Conversely, bone compression or splitting surgery was identified as a protective factor (HR = 0.344, 95% CI = 0.186-0.634). No significant associations were observed for age, reasons for tooth loss, penicillin allergy, use of CGF, or implant characteristics (location, type, length, and diameter). CONCLUSION: After 0-43 months of observation, the CSR for 2620 implants placed in 2023 patients was 95.6% (95% CI = 94.8%-96.4%). Torque < 30 N/cm, non-submerged healing, and Type I bone quality were considered independent risk factors for early implant failure in the anterior region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".