In vitro efficacy of non‐surgical and surgical implant surface decontamination methods in three different defect configurations in the presence or absence of a suprastructure
Bibliographic record
Abstract
OBJECTIVES: Analysis of the in vitro efficacy of non-surgical and surgical dental implant surface decontamination with or without suprastructure. MATERIALS AND METHODS: Three hundred and sixty implants were dipped in indelible red and distributed to 30°, 60°, or 90° angulated bone defect models. One hundred and twenty implants were used for each bone defect, 40 of which were assigned to a decontamination method (CUR: curette; SOSC: soundscaler; APA: air powder abrasion). Of these, 20 were subjected to a simulated non-surgical (NST) or surgical treatment (ST), with/without mucosa mask, of which 10 were carried out with (S+) or without (S-) suprastructure. Uncleaned implant surface was assessed by both-sided implant surface photography. Surface morphology changes were analyzed using scanning electron microscopy (SEM). RESULTS: Cleaning efficacy was significantly better within NST if the suprastructure was removed (p < 0.001). No significant difference was found within ST (p = 0.304). Overall, cleaning efficacy in the order APA > SOSC>CUR decreased significantly (p < 0.0001) for both S+ and S- in NST as well as ST. Separated by NST/ST, S+/S-, defect angulation and decontamination method, only isolated significant differences in cleaning efficacy were present. Linear regression analysis revealed significant associations of remnants with the treatment approach, decontamination method, and defect angle (p < 0.0001). SEM micrographs showed serious surface damage after use of CUR and SOSC. CONCLUSIONS: Suprastructure removal is an additional option to improve cleaning efficacy of non-surgical implant surface decontamination in this in vitro model.
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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".