Evaluation of Trabecular Microstructure of Cancellous Bone Using Quarter-Detector Computed Tomography
Bibliographic record
Abstract
Quarter-detector computed tomography (QDCT) is an ultra-high-spatial-resolution imaging technique. This study aimed to verify the validity of trabecular structure evaluation using a QDCT scanner in the diagnosis of osteoporosis. We used a cancellous bone specimen image of the second lumbar vertebrae of an adult male with moderate osteoporosis. To obtain QDCT images, we created a three-dimensional model from micro-CT images of the specimen. Statistical analysis was performed on the relationship between micro-CT and QDCT imaging modalities. The differences between micro-CT and QDCT were assessed based on their significance with respect to the calculated mean measurements using the Mann-Whitney test. Single regression analysis was performed using linear regression, with micro-CT and QDCT as the explanatory and objective variables, respectively, to determine the relationship of the measured values between the two modalities. By applying the necessary correction to the micro-CT measured values, it is possible to perform an analysis equivalent to micro-CT, which offers higher spatial resolution than QDCT. We found evidence that if QDCT can be used, trabecular structure evaluation may contribute to image diagnosis to evaluate practical bone fragility.
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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 | 0.001 |
| 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) | 0.001 | 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".