Presence of voids after three obturation techniques in band‐shaped isthmuses: a micro‐computed tomography study
Bibliographic record
Abstract
BACKGROUND: The objective of the present micro-computed tomography (micro-CT) study was to assess the presence of voids in band-shaped isthmuses obturated using three different filling techniques. METHODS: Twenty-four artificial molar teeth with a band-shaped isthmus were allocated to three groups (n = 8) for obturation, according to the filling technique: single-cone (SC), continuous wave of condensation (CWC) or lateral condensation (LC). Obturation was performed with gutta-percha (GP) cones and iRoot SP (Innovative Bioceramix, Vancouver, Canada). Post-filling micro-CT scanning was performed. The percentage of filling materials and void volumes were calculated in the isthmus areas and data were analyzed using one-way ANOVA and Tukey tests. RESULTS: The mean percentage of void volumes and corresponding filling percentages in the isthmus areas after obturation in the SC groups was 22.98 % ± 1.19 %, 77.02 % ± 1.19 %; in the CWC groups 10.46 % ± 2.28 %, 89.54 % ± 2.28 %; and in the LC groups was 13.14 % ± 1.85 %, 86.86 % ± 1.85 %, respectively. CONCLUSIONS: In band-shaped isthmus area, the obturation quality of CWC was superior to SC and LC techniques.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".