Comparing factors affecting dental‐implant loss between age groups: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in factors leading to implant failure in older people as the population aged 65 years or older continues to expand. PURPOSE: We sought to identify differences of results in the implant survival rate and the influence of certain factors on implant failure in the older (≥65 years) and younger (<65 years) patients. MATERIALS AND METHODS: Patients who underwent their first dental-implant surgery between July 2008 and June 2018 were included. Data on age, sex, smoking habits, medical conditions, implant location, implant size, and the presence and type of bone graft and membrane were collected and analyzed according to age group. Moreover, cumulative survival rates of implants (by Kaplan-Meier analysis) and hazard ratios (HR) of each factor (using Cox regression analysis with shared frailty) in each group were assessed and results compared between groups. RESULTS: A total of 628 implants in 308 patients and 1904 implants in 987 patients in the older and younger groups, respectively, were assessed, with failure rates of 3.9% and 3.4%. Per Kaplan-Meier analysis, the 11-year patient-level cumulative survival rate of implant treatment was 95.3% (95% CI: 0.91-0.97) in the older and 93.9% (95% CI: 0.88-0.97) in the younger group. The HR for implant failure of the variables, except diameter of dental implants, were not statistically significant in both groups. CONCLUSION: The outcomes of implant treatment were not considerably different between the age groups.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".