Effect of 2% iodine disinfecting solution on bond strength to dentin
Bibliographic record
Abstract
INTRODUCTION: Disinfection of dentin surfaces is desirable so long as it does not interfere with subsequent bonding of adhesive resins. OBJECTIVE: To test the null hypothesis that bond strengths to dentin are not affected by previous application of an iodine disinfecting solution. MATERIALS AND METHODS: Twenty-four extracted non-carious molars were selected. Occlusal enamel was removed producing a flat dentin substrate. Test teeth were all treated with 2% Iodine disclosing/disinfecting solution (I(2)DDS) for 20 sec and rinsed for 20 sec followed by the application of self- or total- etching bonding systems, generating five adhesive groups (n=3): Single Bond; Prime & Bond NT; Clearfil SE Bond; Opti-Bond Plus. The control groups (n=3 per adhesive) had no disclosing/disinfectant application prior to adhesive application. A 4-mm thick resin restoration was built up on each tooth for microtensile testing. Statistical analyses between experimental and control groups were performed by student's t-test (alpha= 0.05). RESULTS: In general, experimental groups (previously treated with I(2)DDS) showed significantly lower bond strength values when compared with their respective controls (p<0.05), except for group Prime & Bond I(2) that did not significantly differ from its control (p>0.05). CONCLUSION: Acetone-base adhesive systems seem not to be affected by the application of I(2)DDS prior to etching and bonding procedures.
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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) | 0.001 | 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) | 0.003 | 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".