Effect of Vacuum Plasma Activation on Early Implant Stability: a Single-Blind Split-Mouth Randomized Clinical Trial
Bibliographic record
Abstract
Objectives: Plasma surface activation of dental implants has been proposed to enhance osseointegration by improving titanium surface chemistry. However, clinical data in humans remain limited. This randomized controlled clinical trial aimed to evaluate the effect of vacuum plasma treatment applied to titanium implants immediately prior to insertion on their stability pattern during the early phases of healing in human subjects. Material and Methods: Motion - MegaGen Implant Co., Ltd.). Implant stability was assessed at placement and at 7, 14, 21, 28, 42, 60, and 90 days using resonance frequency analysis (implant stability quotient [ISQ]). Results: All 48 implants achieved osseointegration at 90 days, with no adverse events or complications. Mean insertion torque did not differ significantly between groups (P = 0.86). Both groups exhibited a typical initial reduction in stability, with the lowest ISQ values at 21 days. Plasma-treated implants showed a more favourable recovery pattern, with significantly higher ISQ values than controls at 60 and 90 days (P = 0.04 and P = 0.03, respectively). The absolute difference in ISQ values between groups remained limited. Conclusions: Vacuum plasma activation of titanium implants may contribute to a more favourable early stability pattern, although both plasma-treated and untreated implants demonstrated predictable osseointegration outcomes. Further research with larger cohorts and longer follow-up is needed to assess the clinical significance of these findings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".