Parafunctional Behaviors and Its Effect on Dental Bridges
Bibliographic record
Abstract
Parafunctional behaviors, especially bruxism, are not uncommon among patient visiting dentists' clinics daily and they constitute a major dental issue for almost all dentists. Many researchers have focused on the definition, pathophysiology, and treatment of these behaviors. These parafunctional behaviors have a considerable negative impact on teeth and dental prothesis. In this review, we focused on the impact of parafunctional behaviors on dental bridges. We summarized the definitions, epidemiology, pathophysiology, and consequences of parafunctional behaviors. In addition, we reviewed previous dental literature studies that demonstrated the effect of bruxism or other parafunctional behaviors on dental bridges and dental prothesis. In conclusion, parafunctional behaviors are common involuntary movements involving the masticatory system. They are more prevalent among children. These behaviors have deleterious effects on dental structures. Causes of parafunctional behaviors include anxiety, depression, smoking, caffeine intake, sleep disorders, or central neurotransmitter dysfunction. Bruxism and other similar masticatory system activity cause dental fracture, loss, and weardown of enamel or teeth. They can also affect different types of dental protheses both fixed and removable types. Parafunctional behaviors shorten the life expectancy of these protheses, and damage residual dentition and denture-bearing tissues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".