Risk factors and reoperative survival rate of failed narrow‐diameter implants in the maxillary anterior region
Bibliographic record
Abstract
BACKGROUND: Narrow-diameter implants (NDIs) have been proposed to address the dilemma of implant installation in cases of insufficient bone volume. However, the potential risk factors of failed NDIs, and whether reimplants with reliable efficacy are still controversial. PURPOSE: This study aimed to evaluate the survival/success rate of NDIs in the maxillary anterior region and that of reimplants at the same site, as well as to explore the potential risk factors of original and replaced implants. MATERIALS AND METHODS: From January 2015 to April 2019, patients receiving NDIs in the anterior maxilla were enrolled in the present study. Multiple variables were assessed to exploit the risk factor of failed NDIs, including age, sex, implant sites, length, surface characteristics, and healing abutment designs. The relationship between bone augmentation and the number of missing teeth was assessed. For failed NDIs, the reasons for NDIs removal and marginal bone loss (MBL) were analyzed. The details and outcomes of reimplants were evaluated. RESULTS: Cumulative survival rates (CSRs) and success rates of 1095 NDIs installed in 835 patients were 96.99% and 96.51%, respectively. In total, 33 of these NDIs failed. TiUnite (TU) surface was a risk factor and it affected the success rate (92.56%) and CSR (92.4%) of NDIs (P < .001). The single NDIs with bone augmentation have lower failure rate. The average MBL for 33 failed NDIs was 1.92 ± 1.91 mm. Additionally, 22 patients with 23 NDIs accepted reimplantation of previously failed NDIs, and the success rate of reimplants was 95.65%. CONCLUSIONS: Surface characteristic (TU surface) was a risk factor for failure of NDIs in the maxillary anterior region. Bone augmentation simultaneously performed during NDIs implantation was favorable for a single missing tooth. As an alternative plan, the reimplantation of failed NDIs was reliable and stable after successful bone reconstruction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".