Estimating the density of femoral head trabecular bone from hip fracture patients using computed tomography scan data
Bibliographic record
Abstract
The purpose of this study was to compare computed tomography density ( ρ CT ) obtained using typical clinical computed tomography scan parameters to ash density ( ρ ash ), for the prediction of densities of femoral head trabecular bone from hip fracture patients. An experimental study was conducted to investigate the relationships between ρ ash and ρ CT and between each of these densities and ρ bulk and ρ dry . Seven human femoral heads from hip fracture patients were computed tomography–scanned ex vivo, and 76 cylindrical trabecular bone specimens were collected. Computed tomography density was computed from computed tomography images by using a calibration Hounsfield units–based equation, whereas ρ bulk , ρ dry and ρ ash were determined experimentally. A large variation was found in the mean Hounsfield units of the bone cores (HU core ) with a constant bias from ρ CT to ρ ash of 42.5 mg/cm 3 . Computed tomography and ash densities were linearly correlated ( R 2 = 0.55, p < 0.001). It was demonstrated that ρ ash provided a good estimate of ρ bulk ( R 2 = 0.78, p < 0.001) and is a strong predictor of ρ dry ( R 2 = 0.99, p < 0.001). In addition, the ρ CT was linearly related to ρ bulk ( R 2 = 0.43, p < 0.001) and ρ dry ( R 2 = 0.56, p < 0.001). In conclusion, mineral density was an appropriate predictor of ρ bulk and ρ dry , and ρ CT was not a surrogate for ρ ash . There were linear relationships between ρ CT and physical densities; however, following the experimental protocols of this study to determine ρ CT , considerable scatter was present in the ρ CT relationships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".