A micro‐computed tomography study of the negotiation and anatomical feature in apical root canal of mandibular molars
Bibliographic record
Abstract
The aim of this study was to investigate the clinical negotiation of various apical anatomic features of the mandibular first molars in a Chinese population using micro-computed tomography (micro-CT). A total of 152 mandibular first molars were scanned with micro-CT at 30 µm resolution. The apical 5 mm of root canal (ARC) was reconstructed three dimensionally and classified. Subsequently, the access cavity was prepared with the ARC anatomy blinded to the operator. The ARC was negotiated with a size 10 K file with or without precurve. Information on the ability to obtain a reproducible glide path was recorded. The anatomical classification of ARC was Type I with 68.45% in mandibular first molars. The negotiation result of ARC with Category i was 387 canals (74.00%). With a bent negotiating file, 96 canals were negotiated, including 88 reproducible glide paths (Category ii) and 8 irregular glide paths (Category iii). About 7.65% canals could not be negotiated with patency successfully (Category iv). The statistical analyze shown the anatomic feature of ARC had effect on the negotiation of ARC (p < 0.05). In conclusion, ARC anatomic variations had a strong potential impact on the negotiation. The category of negotiation in ARC would be helpful in the using of NiTi rotary instruments. Negotiation of ARC to the working length with patency should be careful and skillful because of the complexities of ARC. SCANNING 38:819-824, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".