A systematic review on the correlation between skeletal and jawbone mineral density in osteoporotic subjects
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review was to assess whether the systemic skeletal reduction of bone mineral density (BMD) that characterizes osteoporotic subjects is also associated with a reduction of BMD in the jawbones. MATERIAL AND METHODS: Two reviewers searched independently and in duplicate three databases up to May 2014 and assessed the risk of bias using a tailored version of the Newcastle-Ottawa scale (NOS). Only papers reporting either Pearson's correlation coefficient or Spearman's rank correlation coefficient between skeletal and jawbone mineral density in more than five osteoporotic subjects were selected. RESULTS: From 1763 citations, 64 full-text papers were screened and five papers that met the inclusion criteria were included in the final analysis. None of the included studies complied with all NOS criteria, and as only two studies were eligible for meta-analysis, this was not performed. CONCLUSIONS: Only limited conclusions can be drawn from this systematic review, due to the small number of studies included, their heterogeneity, and their high risk of bias. Future studies that take into consideration both upper and lower jaws, that use the same technique to measure skeletal and jaw BMD (ideally dual-energy X-ray absorptiometry, DXA), and that account for confounding variables (such as medications/diseases affecting bone metabolism and demographics) are needed to provide more robust conclusions.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".