Treatment Planning and Fracture Prediction in Patients with Skeletal Metastasis with CT-Based Rigidity Analysis
Bibliographic record
Abstract
PURPOSE: Pathologic fractures could be prevented if reliable methods of fracture risk assessment were available. A multicenter prospective study was conducted to identify significant predictors of physicians' treatment plan for skeletal metastasis based on clinical fracture risk assessments and the proposed CT-based Rigidity Analysis (CTRA). EXPERIMENTAL DESIGN: Orthopedic oncologists selected a treatment plan for 124 patients with 149 metastatic lesions based on the Mirels method. Then, CTRA was performed, and the results were provided to the physicians, who were asked to reassess their treatment plan. The pre- and post-CTRA treatment plans were compared to identify cases in which the treatment plan was changed based on the CTRA report. Patients were followed for a 4-month period to establish the incidence of pathologic fractures. RESULTS: Pain, lesion type, and lesion size were significant predictors of the pre-CTRA plan. After providing the CTRA results, physicians changed their plan for 36 patients. CTRA results, pain, and primary source of metastasis were significant predictors of the post-CTRA plan. Follow-up of patients who did not undergo fixation resulted in 7 fractures; CTRA predicted these fractures with 100% sensitivity and 90% specificity, whereas the Mirels method was 71% sensitive and 50% specific. CONCLUSIONS: Lesion type and size and pain level influenced the physicians' plans for the management of metastatic lesions. Physicians' treatment plans and fracture risk predictions were significantly influenced by the availability of CTRA results. Due to its high sensitivity and specificity, CTRA could potentially be used as a screening method for pathologic fractures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".