Implant Placement in Patients with Oral Bisphosphonate Therapy: A Case Series
Bibliographic record
Abstract
BACKGROUND: Although the effect of bisphosphonates on dental implant osseointegration is not clear, dental implant failures attributable to oral bisphosphonate therapy have been reported in patients with osteoporosis. PURPOSE: The aim of this study was to evaluate implant survival in patients with a history of bisphosphonate therapy in a retrospective survey. MATERIALS AND METHODS: A total of 46 ITI implants placed in 21 osteoporotic patients (females; average age 53 years, range 42-79 years) were evaluated with regard to probing depth, mobility, thread exposure, and bleeding on probing. All patients were under oral bisphosphonate therapy. RESULTS: None of implants showed mobility and all patients could be considered free from peri-implantitis. Time of bisphosphonate therapy before and after implant insertion showed no statistically significant influence on PD, BOP, and TE. Likewise, implant location, prosthetic type, and opposing dentition had no statistically significant influence on the clinical and radiological parameters of implants. CONCLUSION: Within the limitations of this study, it could be concluded that neither being on oral bisphosphonate treatment before implant placement nor starting bisphosphonate therapy after implant installation might jeopardize the successful osseointegration and clinical and radiographic condition of the implants.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".