Does Salivary Calcium and Phosphate Concentrations Adequately Reflect Bone Mineral Density in Patients with Chronic Periodontitis?
Bibliographic record
Abstract
BACKGROUND: Periodontitis is the inflammation of the periodontal supporting tissues. The response of periodontal tissues to local bacteria leads to bone resorption and destruction of periodontal junction. Given the possible association between periodontitis and low bone mineral density, the aim of present study was to find if measurement of salivary biomarkers as a less invasive method, can provide an appropriate screening method for assessment of bone mineral density in patients with chronic periodontitis? METHODS: A case-control study was conducted on 53 people, including 28 patients with severe chronic periodontitis and 25 healthy people between April 2014 to March 2015 in Zahedan (southeast of Iran). Following Periodontal examination, salivary samples were collected, and the concentration of salivary calcium and phosphate were measured and reported as mg/dl. Bone mineral density of participants was measured using dual energy x-ray absorptiometry and reported as gr/cm2. RESULTS: No significant association was found between concentrations of salivary calcium and phosphate levels with bone mineral density in either healthy people or in patients with severe chronic periodontitis, despite a significant bone density reduction (in the femur neck and lumbar spine L2-L4) in the periodontitis group compared to healthy people (P=0.006, and P=0.009 respectively). CONCLUSION: Concentration of salivary calcium and phosphate do not appear to be good indicators of bone mineral density. Further prospective studies with larger sample size are recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".