In Vitro Analysis of Structural Integrity and Surface Alterations of Reused Healing Abutments
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the changes in healing abutments (HAs) after use based on an in vitro comparison with unused HAs and to evaluate the effectiveness and clinical implications of reusing HAs. MATERIALS AND METHODS: Fifty used HAs were collected from affiliated clinics of the Department of Dentistry at Hanyang University Hospital and analyzed for surface damage, protein contamination, and microgap formation using three-dimensional laser microscopy and microcomputed tomography. The interfacial microgap between the implant and the abutment was measured at different tightening torques (5 N·cm, 10 N·cm, and 15 N·cm). Additionally, bacterial leakage and growth at various tightening torques were assessed by incubating reused HAs over different time periods. RESULTS: Reused HAs exhibited significant surface roughness, protein contamination, and larger microgap compared to unused HAs. The average microgap for reused HAs was 43 μm, whereas unused HAs showed no detectable gap. Bacterial leakage was significantly higher with reused HAs, particularly in those subjected to more than two tightening cycles. Tightening torques of 15 N·cm effectively eliminated the microgap and minimized bacterial leakage, whereas lower torques (5 N·cm and 10 N·cm) resulted in considerable bacterial growth. CONCLUSION: Reusing HAs increases the risk of surface damage, protein contamination, microgap formation, and bacterial leakage, potentially compromising implant treatment outcomes. Higher tightening torque, (15 N·cm) significantly reduces microgap and bacterial leakage at the implant - HA imterface. Clinicians are advised to limit the reuse of HAs. However, if reuse is necessary, an appropriate tightening torque should be applied following a careful assessment of the clinical conditions of each HA.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".