On the ability of experimental impact measures to predict tooth injuries in an <i>ex vivo</i> swine model
Bibliographic record
Abstract
BACKGROUND/AIM: Impact to the orofacial region, in particular teeth, is a frequent incident leading to injury in many sports and can result in health and economic costs for the injured individual. The majority of previous work has applied synthetic models such as plaster or stone, to form analogs of relevant structures to study the potential for impact-induced injury. Biomechanical studies that have applied tissue models (animal or human) for the purpose of determining the biomechanical measures associated with dental injury are rare. The aim of this study was to apply a simple ex vivo model based on swine dentition to ascertain which of a select list of measurable quantities associated with impact mechanics could predict luxation and fracture of teeth due to impact. METHODS: Mandibular central incisors of ex vivo swine dentitions were impacted using a linear drop tower with heights ranging from 1.20 m to 2.42 m. Seven mechanical predictors were assessed at impact and were then subjected to binary logistic regression techniques to determine which was the best predictor of luxations or fractures of the teeth. RESULTS: = 0.722) were found to be statistically significantly different (p < .05), offering the greatest specificity as indicated by receiver operator characteristics. Other measures that are frequently used in impact mechanics, including peak linear acceleration and velocity change, were not statistically significant predictors of tooth injury. CONCLUSION: Identifying mechanical predictors for dental injury of unprotected teeth provides a first step in understanding which aspects of an impact event attribute to dental injury and can lay the foundation for future studies that examine alteration in injury mechanics associated with protection devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".