High Fracture Risk of Femoral Bone Metastasis Treated with Palliative Radiotherapy in Recent Years
Bibliographic record
Abstract
Bone-modifying agents (BMAs) have been widely used to reduce skeletal-related events, including pathological fractures. Herein, we aimed to clarify the incidence of pathological fractures caused by high-risk femoral bone metastases after palliative radiotherapy (RT) in the BMA era and evaluate the necessity of prophylactic surgical stabilization. We assessed 90 patients with high-risk femoral bone metastases, indicated by Mirels’ scores ≥ 8, without pathological fractures and surgical fixations, who received palliative RT at our institution between January 2009 and December 2018. Pathological fracture incidence was analyzed using the Kaplan–Meier method and was 22.8% and 31.0% at 2 and 6 months, respectively. Pathological fractures were caused by 17 of 65 lesions (26.2%) and 9 of 25 lesions (36.0%) in patients who received BMAs and those who did not, respectively (p = 0.44). Additionally, 17 of 42 lesions (40.5%) and 9 of 48 lesions (18.8%) with axial cortical involvement ≥30 and <30 mm, respectively, caused pathological fractures (p = 0.02). The incidence of pathological fractures was high among patients with high-risk femoral bone metastases treated with palliative RT, particularly those with axial cortical involvement ≥30 mm. Therefore, aggressive indications for prophylactic surgical stabilization are warranted for high-risk femoral metastases despite BMA administration.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".