Resistance to fracture of extracted teeth used for pre‐clinical endodontic procedures: Influence of storage conditions
Bibliographic record
Abstract
OBJECTIVES: The purpose of this in vitro study was to determine whether different storage conditions used during endodontic procedures affect the fracture resistance of extracted teeth used for pre-clinical dental education. METHODS: Freshly extracted mandibular incisors and canines were sterilised in an autoclave for 40 minutes at 24°F under a pressure of 20 psi and then stored in distilled water at 4°C until use. Specimens were randomly assigned to two groups based on the storage method used whilst undergoing endodontic procedures. Between endodontic sessions, teeth in the Wet Group (n = 16 incisors; n = 4 canines) were stored in distilled water and teeth in the Dry Group (n = 16 incisors; n = 4 canines) were stored in a dry container. All specimens were kept at room temperature and away from sunlight. Endodontic treatments were performed in 3 sessions over a 3-week period. The specimens were then brought to fracture under compressive forces along the long axis of the tooth in an Instron universal testing machine. The data were analysed using t tests (α = 0.05). RESULTS: None of the teeth fractured during endodontic procedures. However, the compressive load required to fracture teeth stored under wet conditions was significantly higher than the load needed for teeth stored dry (P < .05). CONCLUSIONS: Fracture resistance is affected by storage conditions; teeth stored in water have a higher resistance to fracture than teeth that are stored dry. Fracture resistance was, however, not reduced enough to lead to tooth fracture during pre-clinical endodontic procedures.
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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 | 0.002 |
| 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 | 0.001 | 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".