Advancing Cortical Bone Mapping with global optimization
Bibliographic record
Abstract
Cortical thickness is important in many domains of bone health research. Cortical Bone Mapping (CBM) is a model-based method for measuring cortical thickness in clinical CT, but has not gained traction in the high-resolution peripheral computed tomography (HR-pQCT) community. In this work, we advance CBM by introducing two novel extensions that fit the model globally rather than locally, allowing for spatial regularization of fitted parameters. With two repeat-measures precision HR-pQCT datasets (of the redproximal tibia, distal radius and distal tibia), we show that both the original and novel CBM methods have equivalent or better precision than current standard methods for measuring mean cortical thickness (e.g. for the distal tibia, 0.42-0.44% versus 0.60 %), and that the new methods have lower precision errors in spatially resolved cortical thicknesses (19%-21%) than the original CBM method (46%). Adopting CBM for HR-pQCT analysis could minimize the need for endosteal contour correction, reducing labour burden and improving reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".