Evaluation of the Prevalence and Correlation of Periodontal Bone Status with Associated Risk Factors – A Retrospective Radiographic Study
Bibliographic record
Abstract
Introduction: Radiographic assessment of periodontal bone status is a beneficial diagnostic aid to clinical periodontal examination and may be used in screening of periodontitis caused by interplays between periodontopathogenic bacteria and host's immune response inflicted by risk factors.This study intended to assess the prevalence and correlation of periodontal bone status with associated risk factors.Methods: This retrospective study used the records of patients attended in 2019.Recorded data, such as age, gender, smoking, oral hygiene status and systemic condition, were obtained without including any identifying information.Periodontal bone status was assessed by measuring the proportion of total bone height to the total root length in panoramic radiographs.Data were arranged in tables, and statistically analyzed using Pearson correlation coefficient.Results: Of the total 2,610 patients' panoramic radiographs, 1521 (58.3%) showed periodontal bone loss, while 1089 (41.7%) showed healthy periodontium without periodontal bone loss.The frequency of patients with healthy periodontium was statistically greater in those with good oral hygiene, non-smokers and those without associated systemic diseases.Furthermore, the frequency of patients with mild-to-moderate periodontitis and those with severe periodontitis was statistically greater in smokers, those with associated systemic disease, and those with fair and poor oral hygiene.Conclusion: A significant positive correlation exists between periodontal bone loss and smoking, fair-to-poor oral hygiene and associated systemic diseases.Radiographic assessment of periodontal bone loss is a rapid and accessible tool in periodontitis screening studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".