Efficacy of root filling removal from aged, minimally instrumented mandibular molars using a retreatment system combination
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the efficacy of the ProTaper Universal Retreatment system (PTUR) and XP-endo Finisher R instrument (XPFR) in removing 64-month-old root filling material from minimally-instrumented mandibular molar canals. METHODS: Forty-eight root canals with Vertucci type II or IV configurations from 30 mandibular molars were instrumented to size 20/0.04 taper using Vortex Blue NiTi rotary instruments and irrigated with the GentleWave system. The canals were divided into three groups based on the root filling method: single-cone with AH Plus sealer (SA), single-cone with GuttaFlow 2 sealer (SG), and GuttaCore with AH Plus sealer (GA). Specimens were stored at 100 % humidity and 37 °C for 64 months. Retreatment consisted of PTUR instrumentation, followed by XPFR for canals with residual filling material. Micro-computed tomography was used to evaluate the volume of root filling material in the canals after 64 months of obturation, post-PTUR instrumentation, and after the supplementary XPFR approach. RESULTS: No significant differences were observed in the volume of root filling material among the SA, SG, and GA groups (p > 0.05). After PTUR retreatment, the percentage of filling material removal, or remaining volume, did not significantly differ among the three groups (p > 0.05). However, supplementary XPFR instrumentation significantly reduced the remaining filling material in all groups. The PTUR and XPFR combination was the most effective in the SA and GA groups, followed by the SG group (p < 0.05). CONCLUSION: The PTUR and XPFR retreatment system combination was more effective in removing root filling material from minimally-instrumented mandibular molars. CLINICAL SIGNIFICANCE: The combined use of retreatment file systems improves efficacy of retreating minimally-instrumented mandibular molars by reducing residual filling material and enhancing canal cleanliness, with the potential to improve retreatment outcomes.
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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) | 0.001 | 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".