Jaw Bone Density in Chronic Areca Nut Chewers and Nonchewers Using Digital Panoramic Radiography Indices as a Screening Tool for Osteoporosis: Protocol for a Comparative Evaluation
Bibliographic record
Abstract
BACKGROUND: Jaw bone density can be altered due to various factors including aging, bone pathologies, hormonal levels, medications affecting bone density, and undue stress posed by parafunctional and adverse habits. Of these factors, chronic areca nut chewing, which creates a heavy load on jaw bones, is a commonly encountered adverse habit in patients. Digital panoramic radiography (OPG) indices are an easy and cost-effective method to evaluate jaw bone density. Index values can also be used as a screening tool for osteoporosis. OBJECTIVE: This study aims to compare and evaluate jaw bone density in chronic areca nut chewers and nonchewers using OPG indices as a screening tool for osteoporosis. METHODS: Patients aged 20 years to 40 years reporting to the Department of Oral Medicine and Radiology with and without a history of chronic areca nut chewing will be recruited. OPG will be collected for all recruited patients. The mandibular cortical index, panoramic mandibular index, gonial index, antegonial index, antegoinal notch depth, and mental index will be calculated. RESULTS: The values of these indices will be used to assess and compare osteoporosis in chronic areca nut chewers and nonchewers. Data will be entered and displayed in a tabular format, and correlations between osteoporosis and OPG index values will be determined. The study did not receive any external funding. Recruitment is expected to begin in summer 2025, and publication of the results is expected to occur in late 2026. CONCLUSIONS: Evaluation of jaw bone density in chronic areca nut chewers using OPG indices might prove to be a feasible and cost-effective technique for assessing osteoporotic bone changes. Although the gold standard modality is dual x-ray absorptiometry, its cost and availability create challenges for use in the general population. As osteoporosis of jaw bones does not usually present with symptoms, patients can be made aware of it using the findings from this evaluation. Hence, it will ultimately aid in early detection and prompt interventions, thereby halting disease progress. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/72041.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".