Prevalence of Sinus Tract in the Patients Visiting Department of Endodontics, Kermanshah School of Dentistry
Bibliographic record
Abstract
INTRODUCTION: Sinus tract is one of the manifestations of chronic dental infections, which is a path for the drainage of the infection and pus. The present study was aimed to investigate the prevalence of sinus tract with dental origin analyze the correlation between sinus tract and related factors. METHODS: This study was conducted on 1527 patients, visiting Kermanshah school of dentistry, in 2014.The related teeth were examined in terms of vitality test and exact location of sinus tract. Moreover, the causes of this lesion and the needs for root canal treatment were assessed in these teeth. Having obtained the data from the patients, analyzed by Mann-Whitney, Chi-square tests. RESULTS: The frequency of sinus tract was 9.89% patients. There was a significant correlation between the prevalence of sinus tract and factors such as age, general health status, location of sinus tract and history of root canal treatment. The prevalence of sinus tract in maxilla was higher than the mandible (p=0.087). The prevalence of sinus tract in the posterior teeth (69.54%) was significantly higher than that of anterior teeth (30.46%) (p=0.000). From 724 teeth with periapical inflammation and radiolucency, 9.89% teeth had odontogenic sinus tract, and 23.42% teeth with history of root canal treatment had sinus tract. CONCLUSIONS: The most common cause of sinus tract incidence was previous root canal treatment. Therefore, clinicians need to pay a more attention to examining the posterior teeth referred for endodontic treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".