Dental implant failures in Utah and US veteran cohorts
Bibliographic record
Abstract
INTRODUCTION: Approximately, 5.5 million dental implants are estimated to be surgically placed in the United States yearly, with an anticipated long-term failure rate ranging from 3% to 10%. At the Salt Lake City Dental Clinic within the Department of Veterans Affairs (VHA), specific protocols have been established to mandate that clinicians present every dental implant case for review by a committee. To understand the effectiveness of this approach, a comparative data analysis was undertaken to compare local dental implant failure data against national VHA data. METHODS: Leveraging electronic health records of veterans spanning from 2000 to 2021, we gathered procedural records related to dental implant placement or failure, demographic information, and medical history for individuals who received dental care at various dental clinics within the nationwide VHA network. Subsequently, statistical analyses were conducted using mixed-effects Poisson regression models with cluster-robust standard errors. Incident rate ratios (IRRs) for Utah-specific and nationwide cohorts were ascertained. RESULTS: The Utah VHA dental clinical data showed that there was a slightly lower prevalence of implant failure at 6.7% compared to the national cohort, which had a rate of 6.9%. The implant level failure rates were also low, with 4.20 (confidence interval [CI]: 3.68, 4.81) per 1000 implant placements per year for Utah cohorts. The adjusted IRR indicated a relative 16% reduction in risk among Utah Veterans (IRR 0.84, 95% CI [0.76-0.92]; p < 0.001). CONCLUSIONS: The stringent protocols in place at Salt Lake City, which integrate evidence-based practices and expert opinion for evaluating patient suitability for dental implant placement and subsequent care, contributed to the reduced risk among Utah Dental Clinic veterans pool compared to veterans of other states.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".