Insights Into Early and Late Dental Implant Failures in Veterans—A Retrospective Cohort Analysis
Bibliographic record
Abstract
INTRODUCTION: While numerous studies have examined overall dental implant failure, only a few have focused on the distinct risk factors associated with early (within 6 months of post-implantation) and late (after 6 months of post-implantation) failures, especially in the veteran population. Consequently, understanding the nuances of early and late DIFs specific to veterans is necessary to improve clinical decision-making and enhance VA dental surgeons' ability to mitigate failure risks. METHODS: Electronic health records (EHRs) were obtained between January 1, 2000, and December 1, 2021, for veteran patients aged 18 years or older and those who received a dental implant. Univariable analysis was initially conducted to identify significant risk factors, which were subsequently controlled for in multivariable analysis. A mixed-effect logistic regression model was then used to estimate the odds ratio of early/late failures. The intra-class correlation coefficient (ICC) was calculated using a mixed-effects model to assess the proportion of variance attributable to patient-level clustering. RESULTS: A total of 132 675 US veterans with 468 496 dental implants were included in the multivariable models. Within this cohort, 13 492 failures were reported in 9423 unique veterans, resulting in a 7.1% failure rate over 21 years. Adjusted odds ratios indicated that being 70 years or older at the time of implant placement, Asian race, having osteoporosis, and undergoing reimplantation were significantly associated with increased odds of early implant failure. In contrast, patients aged 40-60 years at the time of placement, African American race, active periodontitis, and alendronate use were associated with increased odds of late implant failure. The ICC of 86% calculated for the cohort indicated a high level of patient-level clustering. CONCLUSION: Veterans with active periodontitis and those using Alendronate exhibited markedly increased odds-approximately 139% and 114% respectively-for late implant failures. Conversely, veterans aged 70 years or older and those undergoing reimplantation had 257% and 89% increased odds respectively in the early failure cohort. The high ICC value for this cohort indicated that the outcome of implant placement was strongly influenced by the patients' prior history of implant failure or success.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".