XP Endo Finisher-R and PUI as supplementary methods to remove root filling materials from curved canals
Bibliographic record
Abstract
This study assessed the ability of XP-endo Finisher R (FKG, La Chaux-de-Fonds, Switzerland) to remove filling remnants from curved mesiobuccal canals of maxillary molars, using the passive ultrasonic irrigation (PUI) technique as a comparison. Twenty-four curved main mesiobuccal canals (MB1) of maxillary molars were instrumented with Wave One (#25/07) and filled with gutta-percha points and AH Plus Sealer. Samples were then re-treated with a standardized protocol with Wave One (#35/06) as the master apical file. Micro-CT scans measured baseline volume of remaining filling material (in mm3). Samples were divided into two groups (n = 12) according to the supplementary cleaning approach: (PUI) or XP-endo Finisher R. Statistics compared baseline and final volume of filling material (within-group); and the percentage of filling material reduction (between-group). Mean baseline volumes, final volumes, and percentages of reduction (%) of filling material for XP-endo Finisher R and PUI were respectively: 0.060 mm3, 0.042 mm3, and 31.28%; and 0.064 mm3, 0.054 mm3, and 16.57%. Both tested protocols reduced the amount of filling material (p < 0.05). XP-endo Finisher R had higher percentage of reduction as compared to PUI (p < 0.05). XP-endo Finisher R and PUI used as supplementary cleaning protocols during re-treatment improved the removal of root filling material in curved canals; but XP-endo Finisher R was approximately twice more efficient. The complete filling material removal during re-treatment procedures is still a challenge. Supplementary cleaning protocols may help to remove the remaining material after the complete mechanical preparation of curved canals. XP-endo Finisher R was approximately twice more efficient than PUI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".