The Effect of Sealer Application Methods on Voids Volume after Aging of Three Calcium Silicate-Based Sealers: A Micro-Computed Tomography Study
Bibliographic record
Abstract
During obturation, air voids are undesirable as they may provide shelter for microorganisms or passage for fluids. This study aimed to compare the occurrence of voids between three calcium silicate-based sealers (CSBSs) (MTA-Fillapex, BioRoot-RCS, Bio-C) and the change in their volume after aging. In addition, we aimed to compare voids when using two sealer application methods: lentulo-spiral (LS) and gutta-percha (GP) cone. Thirty extracted mandibular premolars (n = 30) were endodontically prepared and obturated using single GP cone (SGPC) technique. Each sealer was applied to 10 teeth (n = 10) using LS or GP. Micro-computed tomography (micro-CT) was used to quantify the volume of root filling and voids before and after 8-week storage in a phosphate-rich medium. The percentage of root filling and voids were compared between the groups using a Mann–Whitney U test and Kruskal–Wallis test with a Bonferroni correction. Before aging, the percentages of root filling volume after obturation were comparable with no significant differences between sealers (p = 0.325) or application methods (p = 0.950). After aging, the voids’ volume increased significantly in all sealers (p ≤ 0.05). However, no significant differences were found between sealers (p = 0.302). In conclusion, voids in CSBSs may not reduce in size with aging; hence, SGPC should be carefully selected for suitable cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".