Evaluation of three obturation techniques in the apical third of mandibular first molar mesial root canals using micro-computed tomography
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Recent studies have demonstrated a high incidence of isthmuses in mandibular first molar mesial roots, and intratubular mineralization following mineral trioxide aggregate obturation. This study assessed the filling quality of three obturation techniques in the apical 5 mm of mandibular first molar mesial root canals. MATERIALS AND METHODS: Sixty extracted human mandibular first molar mesial roots with two separate canals that had interconnecting isthmuses, were prepared to an apical size of 40/0.06. They were allocated to three groups of 20 roots for obturation by either cold lateral compaction (CLC) or the continuous wave of condensation (CW) that used gutta-percha and AH Plus sealer, or by an orthograde canal obturation using OrthoMTA. The obturated roots were scanned by micro-computed tomography and assessed for the volumetric ratio (%) of gutta-percha, sealer, and OrthoMTA within the main canals or isthmuses in the apical 5 mm area. Measurements were analyzed statistically for differences among three obturation techniques. RESULTS: In the main canals, filled volume ratios were not significantly different among groups. Within isthmuses, the filled volume ratio for CLC was lower than in CW (P = 0.025) or OrthoMTA (P = 0.002). In isthmuses, the gutta-percha volume ratio in CLC was lower than in CW (P = 0.005), although the sealer volume ratio was higher than in CW (P = 0.049). CONCLUSION: CLC demonstrated lower filling densities in isthmuses in the apical region than either CW or OrthoMTA. Orthograde MTA obturation showed comparable filling quality to gutta-percha with sealer.
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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) | 0.000 | 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 | 0.000 | 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".