Relationship Between the Applied Occlusal Load and the Size of Markings Produced Due to Occlusal Contact Using Dental Articulating Paper and T-Scan: Comparative Study
Bibliographic record
Abstract
Background: The proposed experimental design was devised to determine whether a relationship exists between the occlusal load applied and the size of the markings produced from tooth contact when dental articulating paper and T-Scan are interposed alternatively. Objective: The objective of our study was to compare the relationship between contact markings on an articulating paper and T-Scan for an applied occlusal load. Methods: In this in vitro study, dentulous maxillary and mandibular dies were mounted on a metal jig and articulating paper and T-Scan sensor were placed alternatively between the casts. Loads simulating occlusal loads began at 25 N and incrementally continued up to 450 N. The resultant markings (180 marks resulting from articulating paper and 138 from T-Scan) were photographed, and the marks were analyzed using MOTIC image analysis and sketching software. Descriptive statistical analyses were performed using one-way analysis of variance, Student t test, and Pearson correlation coefficient method. Results: Statistical interpretation of the data indicated that with articulating paper, the mark area increased nonlinearly with increasing load and there was a false-positive result. The characteristics of the paper mark appearance did not describe the amount of occlusal load present on a given tooth. The contact marking obtained using T-Scan for an applied occlusal load indicated that the mark area increased with increase in the load and provided more predictable results of actual load content within the occlusal contact. Conclusions: The size of an articulating paper mark may not be a reliable predictor of the actual load content within the occlusal contact, whereas a T-Scan provides more predictable results of the actual load content within the occlusal contact.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".