Marginal gaps and internal voids after root-end filling using three calcium silicate-based materials: A Micro-CT analysis
Bibliographic record
Abstract
This study evaluated the 3D quality of root-end filling, assessing the presence (volume and percentage) of marginal gaps and internal voids formed after retro-filling with three calcium silicate-based materials: MTA Angelus (Angelus Soluçoes Odontologicas, Londrina, PR, Brazil), Biodentine (Septodont Ltd., Saint Maur-des-Faussés, France) and Neo MTA Plus (Avalon Biomed Inc., Bradenton, Florida, US). Thirty human, extracted, single rooted teeth were used. Orthograde root canal treatment, root resection (3mm shorter than the apex) and retrograde cavity preparation with ultrasonic tips were performed. Teeth were divided into 3 groups (n =10 each) following a stratified randomization according to the initial volume of the root-end cavity. After retrofilling, samples were stored for 7 days. Then, two rounds of micro-CT scans were performed: soon after root-end preparation (with the cavity still empty) and 7 days after root-end filling. Marginal gaps, internal voids volume (mm3 and %), as well as, the overall defects (sum of gaps and voids) were evaluated. Statistics compared the three groups in relation to those defects. There was not statistical difference between groups regarding the marginal gaps (P≥ 0.05), the internal voids (P≥ 0.05), and the overall defects (P≥ 0.05). Median (mm3) and % of overall air-entrapment defects (gaps and/or voids) was: 0.004mm3 and 1.749% for MTA Angelus, 0.018mm3 and 6.660% for Biodentine, and 0.012mm3 and 4.079% for Neo MTA Plus. All materials had gaps and/or voids. No differences were found between MTA Angelus, Biodentine and Neo MTA Plus.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".