Bacterial penetration along different root canal filling materials in the presence or absence of smear layer
Bibliographic record
Abstract
AIM: To study the effect of the smear layer on the penetration of bacteria along different root canal filling materials and to examine the dentine/sealer and sealer/core material interfaces for the presence of bacteria. METHODOLOGY: A total of 110 human root segments were instrumented to size 80 under irrigation with 1% sodium hypochlorite. Half of the roots were irrigated with a 5-mL rinse of 17% EDTA. Roots with and without smear layer were filled with gutta-percha (GP) and AH Plus sealer (AH), GP and Apexit sealer (AP), or RealSeal cones and sealer (RS). Following storage in humid conditions at 37 degrees C for 7 days, the specimens were mounted into a bacterial leakage test model for 135 days. Survival analyses were performed to calculate the median time of leakage and log-rank test was used for pairwise comparisons of groups. The level of significance was set at P = 0.05. Selected specimens were longitudinally sectioned and inspected by scanning electron microscopy for the presence of bacteria at the interfaces. RESULTS: In the presence of the smear layer, RS and AP leaked significantly more slowly than in its absence. In the absence of the smear layer, AH leaked significantly more slowly than RS. SEM results indicated a differential pattern of bacterial penetration among the sealers. CONCLUSIONS: Removal of the smear layer did not impair bacterial penetration along root canal fillings. A comparison of the sealers revealed no difference except that AH performed better than RS in the absence of the smear layer.
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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".