Prevalence of defects and fractures in nickel-titanium instruments after single use in patients
Bibliographic record
Abstract
Abstract We investigated defects and fractures in nickel-titanium, engine-driven, endodontic instruments/files that had been single-used in patients` mandibular and maxillary molars. A total of 169 instruments [n = 113 ProTaper Next® (PTN) (Dentsply Maillefer, Ballaigues, Switzerland), and n = 56 Reciproc® (R) (VDW, Munich, Germany)] were analyzed using scanning electron microscopy. A single operator had used the instruments under a standardized protocol in one patient`s molar. Two trained and calibrated evaluators analysed three locations for each instrument. For non-fractured instruments, each location/third (apical/medium/coronal) received one of the classifications: i) intact (no plastic deformation/no defect), or ii) crack and/or deformation/unwinding. For fractured instruments, the area of fracture was classified in: i) cyclic fatigue mode, or ii) shear mode (torsional fatigue). Chi-square test calculated frequency of defects. PTN and R presented the same low frequency of defects after one clinical use in patients` molars by the same dentist. Defects appear equally in apical, medium, and coronal – except for X3 PTN that had more defects in the coronal part. Only one clinical fracture occurred, with an X3 PTN instrument: a torsional fatigue fracture originated in a crack. It is appropriate to use either PTN or R, in a single use, to treat root canals of patients` molars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".