Relation between Spongy Bone Density in the Maxilla and Skeletal Bone Density
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Osteoporosis is a disease affecting more than 300 million people worldwide and is responsible for numerous medical complications. This study aimed to investigate the relation between skeletal and maxillary bone density. MATERIALS AND METHODS: Seventy-three patients were recruited and divided between group A (osteoporosis), group B (healthy, control), and group C (osteopenia) on the basis of a dual-energy x-ray absorptiomery (DXA) scan. These patients also received a CT scan on which bone density measurements were performed at five sites: maxilla midline, retromolar tuberosities, incisor, premolars, and molar regions. RESULTS: The bone density was lower in osteoporotic patients compared with the control patients. The bone mineral density (BMD) of the tuberosities showed the strongest correlations with the BMD of the hip and the spine (respectively, r = 0.50 and r = 0.61). The midline region showed moderate correlations with the hip (r = 0.47) and the spine (r = 0.46). For potential implant sites, the correlations with the BMD of the hip and spine were, however, small to insignificant. Based on measurements of bone density of the maxilla, it was possible to predict if the patient was osteoporotic or not with a sensitivity of 65% and a specificity of 83%. CONCLUSIONS: The maxillary bone density of subjects with osteoporosis is significantly lower than that of healthy patients. Moreover, there is a direct correlation between the density of the skeleton and the density of some sites of the maxilla. Using measurements of maxillary bone density in order to predict skeletal bone density might be a useful tool for the screening of osteoporosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".