Tooth Tissue Loss in Locating Mesiobuccal Canal during Selective Retreatment using Dynamic Navigation System: An In vitro Study
Bibliographic record
Abstract
Selective root canal retreatment has demonstrated positive success, but the process is challenging because imprecision compromises the tooth's structural durability. As there is a lack of literature on selective retreatment using DNS, this in-vitro study was designed for comparative evaluation of tooth tissue loss and time taken in locating mesiobuccal (MB)canal using the dynamic navigation system (DNS; Navident, ClaroNav, Toronto, ON, Canada ) to the freehand (FH) method. The null hypothesis was that both methods would have similar tooth tissue loss and time required for the procedure. Twenty root canal-treated human mandibular molar teeth were mounted on Navident manikin. Using the Navident programme, the drilling path and depth were virtually designed using cone-beam computed tomographic (CBCT) scans. A minimal access cavity for locating the MB root was prepared with dynamic navigation in the DNS group and with freehand in the FH group. Volumetric tooth tissue loss was evaluated with the help of postoperative CBCT and On-demand software. Statistical analysis was done with an independent-sample T Test (p<0.05). The time taken for the procedure was recorded with a stopwatch. The tooth tissue loss was significantly less with the DNS group (35.83 mm3) compared to the FH group (52.84 mm3) with a P value of 0.001. The time taken for the DNS group was less with the DNS group (29.00 seconds) compared to the FH group (53.60 seconds) with statistical significance with a P value of 0.001. The DNS resulted in minimal tooth tissue loss with a shorter time compared to the FH group. This technique can be practised for predictable selective retreatment in endodontics.
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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 | 0.001 |
| 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".