Building osteoporosis prevention into dental practice.
Bibliographic record
Abstract
OBJECTIVES: The National Report Card on Osteoporosis Care (2008) announced the need for comprehensive approaches to risk reduction and improvement in the early diagnosis of osteoporosis. Dental research has suggested that low systemic bone-mineral density also occurs in alveolar bone, and people with osteoporosis may have an increased risk of tooth loss. Whether or not a causal link exists, both conditions share similar modifiable risk factors, including a role for calcium and vitamin D. The purpose of this paper was to critically examine the role calcium and vitamin D play in the relationship between osteoporosis and the risk of tooth loss. METHODS: Scientific articles were obtained through PubMed, MEDLINE, CINAHL, AgeLine and Web of Science. Publications were restricted to those involving human subjects, and English-language articles on calcium and vitamin D. The search yielded 8 articles relating to osteoporosis and tooth loss that included calcium and vitamin D intake. RESULTS: Despite methodological concerns, the evidence shows a relationship between osteoporosis and tooth loss for people who have an inadequate intake of calcium and vitamin D. Adequate calcium intake positively influences optimal peak bone mass and may also assist in tooth retention in later life. CONCLUSIONS: The dental sector can assist with national prevention strategies for osteoporosis care.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".