Effect of Different Disinfection/Sterilization Methods on Risk of Fracture of Teeth Used in Preclinical Dental Education
Bibliographic record
Abstract
The aim of this in vitro study was to determine whether different disinfection/sterilization methods affected the risk of fracture of extracted teeth used for preclinical dental education. Freshly extracted intact mandibular incisors were assigned to different groups according to the processing method used. In the autoclave group (n=20), teeth were autoclaved for 40 min at 240°F under a pressure of 20 psi; in the formalin group (n=20), teeth were immersed in 10% formalin for two weeks; and in the control group (n=10), teeth were not processed. Teeth were then stored at 4°C in distilled water until use. Endodontic procedures were performed, and the fracture strength of the specimen was subsequently tested under compressive force along the long axis of the teeth using an Instron universal testing machine. The results showed that none of the specimens fractured during endodontic procedures. However, the compressive load needed to fracture the teeth was significantly less for the autoclaved teeth than the teeth stored in formalin or the control teeth (p<0.001). The disinfection/sterilization method used affected the fracture resistance of extracted teeth: autoclaved teeth were less resistant to fracture than teeth that were not sterilized or teeth that were chemically disinfected. However, fracture resistance was not reduced enough to lead to tooth fracture during preclinical endodontic procedures. Therefore, either processing method may be appropriate for teeth to be used for preclinical endodontic training.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".