CT-based internal density calibration for opportunistic skeletal assessment using abdominal CT scans
Bibliographic record
Abstract
CT-based opportunistic skeletal assessment complements current osteoporosis diagnosis. Quantitative assessment by internal density calibration overcomes the limitations of phantom-based calibration. We sought to establish and validate an internal calibration technique using abdominal CT scans and establish reproducibility precision for three density calibration techniques. Ten full-body cadavers were CT scanned at the spine and pelvis with a calibration phantom. Internal calibration was performed using in-scan tissue references and deriving a voxel-specific calibration. Bone mineral density (BMD) and finite element (FE) failure load assessed skeletal health. Three independent users measured intra-exam precision by manual tissue selection. To verify results, ten subjects were imaged using an abdominal imaging protocol. Internal calibration performed equivalently to gold-standard phantom-based calibration in the cadaver spine and hip. Internal calibration BMD precision in the spine was 7 mg/cc (4.9%) and FE precision was 163 N (7.2%), whereas phantom-based precision was 3 mg/cc (1.8%) and 77 N (3.8%). Internal calibration hip BMD and FE precision was 11 mg/cc (5.3%) and 84 N (6.0%), whereas phantom-based precision was 2 mg/cc (1.3%) and 30 N (3.4%). Using the abdominal imaging protocol, internal calibration performed comparably to phantom-based calibration. Internal calibration provides BMD and FE outcome precision within 7.2% for opportunistic skeletal health assessment.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".