The impact of antiplatelet and anticoagulant medications on early implant failure following sinus floor augmentation: A retrospective cohort analysis
Bibliographic record
Abstract
INTRODUCTION: The effect of antiplatelet and anticoagulant medications on the outcomes of sinus floor augmentation remains unclear. METHODS: This retrospective cohort study analyzed data from electronic medical records of consecutive patients undergoing sinus floor augmentation at a single medical center. Patients were categorized into three categories: patients under antiplatelet medications, patients under anticoagulation medications, and healthy individuals. Data collected included tobacco smoking, residual alveolar bone height, timing of implant placement, materials used, vertical bone gain, early implant failure (EIF), and complications such as Schneiderian membrane perforation and postoperative bleeding. Multivariable analysis was performed to assess risk factors for EIF. Statistical significance was considered below 5%. RESULTS: Among 110 patients with 305 implants, EIF occurred in 10% of patients and 4.65% of implants. No significant difference in postoperative bleeding or EIF was found between study groups. Univariate and multivariable analyses highlighted tobacco smoking (odds ratio [OR] = 7.92), lower residual alveolar ridge height (OR = 0.81), and staged implant placement (OR = 4.64) as significant EIF risk factors in this cohort. CONCLUSIONS: Anticoagulant and antiplatelet therapies do not significantly elevate the risk of EIF or postoperative bleeding following sinus floor augmentation. Tobacco smoking, residual alveolar ridge height and staged sinus floor augmentation were risk factors for EIF in patients using antiplatelet or anticoagulation medications undergoing sinus floor augmentation.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".