Systemic Conditions and Medication Use in Older Patients Undergoing Dental Implants: A Nationwide Cross‐Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Dental implants are widely utilized to manage both partially and completely edentulous older patients. However, such patients often present with multiple systemic diseases and may be at an increased risk of complications before and after implant surgery. Nevertheless, population-level data on systemic diseases and medication use in these patients remain limited. METHODS: This retrospective, cross-sectional study analyzed 36 957 patients who underwent 43 171 insurance-covered implant surgeries between 2014 and 2019 using the National Health Insurance Service-National Sample Cohort (NHIS-NSC) database. Patients aged 65 years or older were included. Sociodemographic characteristics, diagnosis of systemic diseases within 1 year before implant surgery, medication history, and type of medical institution were evaluated. Additionally, logistic regression analysis was performed to investigate factors associated with implant removal. RESULTS: Among 36 957 patients who underwent 43 171 implant surgeries, implant removal occurred in 803 patients. Within 1 year before surgery, 89.33% had at least one systemic disease, including hypertension (57.92%), arthritis (43.39%), and diabetes (34.62%). Antithrombotic and antiresorptive agents were prescribed to 6.77% and 5.05% of patients, respectively. The use of intravenous (IV) bisphosphonates, denosumab, and direct oral anticoagulants (DOACs) increased, whereas the use of oral bisphosphonates and warfarin decreased. Logistic regression analysis showed that cerebrovascular and kidney disease increased the risk of implant removal, whereas osteoporosis and antiresorptive agents decreased the risk. CONCLUSION: Most older patients who underwent implant surgery had systemic diseases, and approximately 10% were prescribed medications. Cerebrovascular and kidney diseases increased the risk of implant removal, whereas osteoporosis or antiresorptive therapy decreased the risk. With the increasing use of DOACs, IV bisphosphonates, and denosumab, clinicians carefully review the medical histories of older implant patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".