Optimizing fracture management: Correlating the physical and mechanical properties of bone to computed tomography to generate an estimate of bone quality
Bibliographic record
Abstract
Non-invasive estimates of bone quality are of great interest as they could potentially improve the diagnosis and treatments for bone disorders, such as osteoporosis, and could aid in preoperative planning of surgical interventions, improving patient outcomes in orthopaedic trauma.Quantitative computed tomography (QCT) is currently being investigated as a method of estimating structural bone mineral content (sBMC) and, thus, bone quality.However, the reported correlations between QCT and sBMC have been variable and the relationships reported between sBMC and the apparent elastic modulus (E*) of bone have been even more variable.A series of investigations are presented, relating QCT, sBMC, apparent density ( APP ) and the E* of bone.These studies were performed with the ultimate objective of determining the appropriateness of QCT as a predictor of the E* of bone and whether the E* of bone could be predicted from information regarding the sBMC and APP .The repeatability of the CT scanner and the protocol for scanner calibration were studied to determine the optimal method for calibrating QCT scans.It was determined Dr. John Schreiner, who was always willing to go all the way back to the basics to address my questions, and interested in hearing of any progress.On this line, I would also like to thank Dr. Manuela Kunz for her assistance and expertise in performing the registration of the specimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".