Efficacy of reciprocating and rotary techniques for removing filling material during root canal retreatment
Bibliographic record
Abstract
AIM: To compare the efficacy of reciprocating and rotary techniques with that of hand files for removing gutta-percha and sealer from root canals. METHODOLOGY: The root canals of fifty-four human extracted maxillary central incisors were cleaned and shaped using a crown-down technique to a size 40 and filled with gutta-percha and a zinc oxide-eugenol-based sealer using a lateral compaction technique. Teeth were divided into three groups according to the technique used for removing the root filling material: group I - Gates-Glidden burs and stainless steel hand files up to size 50; group II - rotary technique with NiTi Mtwo R files and additional Mtwo files to size 50, 0.04 taper; group III - reciprocating technique with the Reciproc instrument R50, size 50, 0.05 taper. Chloroform was used as a solvent in all groups. Teeth were then split longitudinally and photographed under 8× magnification. The images were transferred to a computer, and the total canal space and remaining filling material were quantified. The ratio of remaining filling material to root canal periphery was computed with the aid of Image Tool 3.0 software. The mean percentages of remaining filling material and time required to remove it were compared using the Kruskal-Wallis and Mann-Whitney tests (P < 0.05). RESULTS: The mean percentage of remaining filling material was significantly higher (P < 0.05) in group II, with Mtwo rotary files (12.17%), than in group I, with the hand file technique (7.19%), and group III, with Reciproc instruments (4.57%), which were statistically similar (P > 0.05). The time required to remove filling material was significantly shorter (P < 0.05) in group III (194 s), followed by group II (365 s) and group I (725 s) (P < 0.05). CONCLUSION: Remaining endodontic filling material was observed on the canal walls of all teeth regardless of the technique used. Hand files combined with Gates-Glidden burs (group I) and the reciprocating technique (group III) removed more filling material from the canal walls than the Mtwo R files. The reciprocating technique was the most rapid method for removing gutta-percha and sealer, followed by the rotary technique and the hand file technique.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".