Accuracy of Quantitative Computed Tomography Bone Mineral Density Measurements in Mandibles: A Cadaveric Study
Bibliographic record
Abstract
PURPOSE: The aim was to investigate the accuracy of quantitative computed tomography bone mineral density (BMD) measurements in mandibles, comparing measured BMD with calibrated BMD. MATERIALS AND METHODS: Seventy mandibles from adult cadavers were used. Twenty tomographic cuts were made in each mandible. In each tomographic cut, a region of interest was located, and the bone density was measured in Hounsfield unit (HU). A polymethyl methacrylate phantom containing four inserts of different predetermined densities (hydroxyapatite 100, 200, 500, and 700 mg/cm(3) ) was used to calculate calibrated bone density. Correlation between measured and calibrated bone densities was calculated. RESULTS: Mean total correlation between measured and calibrated BMD in the 20 sagittal tomography cuts showed almost perfect positive correlation (r = 0.998, p < .001). However, when average BMD measurements in HU were compared, the measured total BMD (in the 20 sagittal tomography cuts studied) was 54.99 ± 421.59, whereas the total calibrated BMD was 49.28 ± 364.95, with statistically significant difference (p = .001). CONCLUSIONS: There are discrepancies between measured and calibrated BMD; in this sense, a calibrated bone phantom with a predetermined mineral density should be used to determine the exact BMD before dental implants surgery.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".