Endodontic dynamic navigation for precise apical microsurgery: Case report
Bibliographic record
Abstract
Retrieval of separated file at the periapex with minimal intervention is highly demanding. Preserving the remaining healthy periapical bone of a tooth with large lesion to promote healing is the strategic treatment plan. Endodontic DNS (Navident, ClaroNav, Toronto, ON, Canada) was used for precise apical microsurgery in two such clinically complex cases. Scanty literature is available on the use of DNS in apical microsurgery of such similar conditions. Case 1 complained of separated instruments with moderate pain during mastication in root canal treated 14. Two-dimensional (2D) and three-dimensional imaging revealed two separated endodontic files: one in the apical third and another in the periapex of the buccal root. The absence of periapical lesion here demanded minimal ostectomy for surgical removal of separated instruments. Minimal osteotomy, resection of the root tip, and retrieval of the apical separated instrument were achieved with a single precise cut assisted with DNS. During the retro-cavity preparation, the second file was also retrieved atraumatically using ultrasonics. Case 2 complained of moderate pain and mobility in 12. Clinical examination revealed slight discoloration in 12. 2D and 3D imaging revealed a large periapical lesion. Apical microsurgery with endodontic dynamic navigation resulted in the precise, simultaneous location, and resection of the root tip along with the management of the apical pathology with minimal invasion. This was possible only because of DNS. Both cases demonstrated uneventful healing at 1-year review. Periapical radiographs revealed a healthy periapical region in case 1 and healing periapical region in case 2.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".