Metastatic Burst Fracture Risk Prediction Using Biomechanically Based Equations
Bibliographic record
Abstract
Clinical guidelines are a useful adjunct to select patients with spinal metastases for prophylactic intervention. The objective of this study is to determine the ability of biomechanically based models to accurately predict metastatic burst fracture risk. Ninety-two vertebrae with osteolytic spinal metastases were examined retrospectively. Vertebrae were categorized as burst fractured, wedge fractured, or intact and analyzed using three predictive models: vertebral bulge (maximum radial displacement under load), vertebral axial displacement (maximum axial displacement under load), and a volumetric estimate of tumor size. The load-bearing capacity parameter (tumor volume, bone mineral density, disc quality, pedicle involvement) was determined from computed tomography while the load-bearing requirement parameter (pressure load, loading rate) was determined using computed tomography and patient records (retrieved for 37 patients [52%]). Fracture prediction was optimized using the vertebral bulge model considering only load-bearing capacity with a specificity, sensitivity, and confidence interval of 1 to yield a clear threshold for burst fracture risk. Fracture prediction in the other two models, vertebral axial displacement considering only load-bearing capacity and tumor size, also was strong with receiver-operator curve values of 0.992 and 0.988, respectively. The predictive power of these models can provide useful clinical information for prophylactic decision-making.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".