Micro‐CT evaluation of residual material in canals filled with Activ GP or GuttaFlow following removal with NiTi instruments
Bibliographic record
Abstract
AIM: To assess the efficacy of removing Activ GP or GuttaFlow from canals using NiTi instruments. METHODOLOGY: Root canals in 55 extracted pre-molars were prepared to apical size 40, 0.04 taper. The teeth were imaged with micro-CT, and 30 teeth selected that had consistent apical size and taper of the shaped canals. They were randomly assigned to root filling with either the glass-ionomer-based ActivGP system (n = 15) or the polyvinylsiloxane-based GuttaFlow system (n = 15). After 2 weeks, canals were retreated stepwise with size 40-50 EndoSequence 0.04 taper instruments. Micro-CT scans (8 mum) were taken after use of each instrument to detect root filling residue in the coronal, middle and apical segment, and the retreatment time recorded. Residue, expressed as percentage of canal surface area, was compared between groups with t-tests, and within groups with repeated measures anova and Bonferroni-adjusted pairwise comparisons. Retreatment time was analysed with one-way anova. RESULTS: The percentage of sealer residue-coated canal surface was consistently highest (P < 0.001) in the apical third of canals, and it did not differ significantly between the two root filling groups. Stepwise enlargement from size 40 to 50 significantly decreased the amount of sealer residue in both groups (P < 0.001). Retreatment time did not differ significantly between groups. CONCLUSIONS: Both root fillings with ActivGP and GuttaFlow were removed with nickel-titanium rotary instruments. Enlargement of canals up to two sizes beyond the pre-retreatment size was necessary to minimize the amount of sealer remaining.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".