Ability of Calcium Silicate and Epoxy Resin-based Sealers to Fill the Artificial Lateral Canals in the Presence or Absence of Smear Layer
Bibliographic record
Abstract
Background: The lateral canals are pathways for bacteria and their products to pass between the root canal and the periradicular tissue.Objectives: The present study aims to compare the filling ability of the lateral canals with three calcium silicate based sealers, including MTA Fillapex, Endoseal MTA and Sure-Seal Root, and AH26 epoxy resin sealer in the presence and absence of the smear layer.Material and methods: Six lateral canals were prepared using an engine reamer in 80 single-rooted human teeth.The root canals were cleaned, then the teeth were randomly divided into two groups.In group A, the smearlayer was removed using 17% EDTA and 5.25% NaOCl, and in group B, the canals were irrigated with normal saline.Groups A and B were divided into four subgroups each, according to the sealer used: A1, B1 (MTA Fillapex sealer), A2, B2 (Endoseal MTA sealer), A3, B3 (Sure-Seal Root sealer), and A4, B4 (AH26 sealer).Obturation of canals was conducted by the warm vertical technique and then teeth were incubated for 72 hours.Teeth were made clear and the filling of lateral canals were evaluated under stereomicroscope.Results: In group A, the highest lateral canals filling rate was in subgroup A2 and the lowest in subgroup A1, and the difference was statistically significant.However, in both groups, the highest lateral canals filling rate
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".