<i>In vitro</i>shearing force testing of two seventh generation self-etching primers
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the in vitro shearing force performance of orthodontic attachments using two self-etching primers (SEPs): iBOND and G-Bond. DESIGN: In vitro, laboratory study. MATERIAL AND METHODS: One hundred and eighty human molars were randomly divided into four groups and again into three sub-groups with 15 teeth each. Teeth were bonded with a stainless steel button (GAC International,Bohemia, NY, USA) using Transbond XT adhesive composite. The bonding agents were iBOND, G-Bond, Transbond Plus SEP and Transbond XT primer. Shearing force tests were carried out immediately, and at 24 hours and 3 months using a universal testing machine. Force to debond (N) and Adhesive Remnant Index (ARI) scores were evaluated and compared. RESULTS: Transbond XT primer required a higher immediate (P<0·05)force to debond when compared to the Transbond Plus SEP, iBOND and G-Bond.After 24 hours, mean force to debond for Transbond XT primer and Transbond Plus SEP showed significant increases. At 3 months, all four bonding agents demonstrated force levels to debond that were not significantly different from one another. Furthermore, comparison of ARI scores indicated a significant difference between the groups at all time points. CONCLUSIONS: iBOND and G-Bond may well sufficiently with stand the alignment and occlusal forces imparted by light archwires during immediate archwire tie-in and over the initial levelling and alignment phase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".