Prevalence of Pulp Stones in Patients Visiting the Dental Hospital of Imam Abdulrahman Bin Faisal University: A Correlative Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: Dental pulp stones are distinct calcified bodies that can be found in teeth that are healthy, diseased, or even unerupted. Pulp stones are suggested to be a manifestation of various systemic and genetic diseases affecting different organs of the body. Therefore, this study aimed to correlate the prevalence of pulp stones with gender, nationality, age, dental status, and systemic diseases. METHODS: The medical records and radiographs of patients who visited the screening clinics and the Department of Oral Diagnosis at the College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia, between January 2017 and June 2018 were analyzed in this study. Two examiners evaluated the digital orthopantomographs (OPGs) to identify the prevalence of pulp stones concerning the patient's age, gender, nationality, arch position, and medical condition. RESULTS: A total of 153 patient records were examined, and pulp stones were detected in 43.1% of the patients. Among the nationalities, Saudi patients were the most affected at 57.6%, while 42.4% were non-Saudi. The maximum occurrence of pulp stones was observed in age group 4 (9.2%), while the minimum occurrence was in age group 8 (0.7%). The maximum occurrence of pulp stones (21.2%) was observed in age group 4 (36-45 years), while the minimum occurrence was 7.6% in age group 2 (16-25 years). Out of all examined patients, 46 (30.1%) patients were medically compromised. Among these medically compromised patients, radiographic examination showed that 56.5% (n=26) had pulp stones. CONCLUSION: This study supports the idea that dental radiographs are useful in detecting pulp stones. Further research is required to explore the potential of using dental radiographs as a screening tool for the early detection of systemic diseases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".