Pathologic fractures correlate with reduced survival in patients with malignant bone disease
Bibliographic record
Abstract
BACKGROUND: Data from randomized, controlled trials of zoledronic acid were retrospectively analyzed to assess the effect of pathologic fractures on survival in patients with malignant bone disease. METHODS: A Cox regression model was used to estimate the effect of fractures (time-dependent variable) on survival in patients with stage III multiple myeloma or bone metastases from solid tumors enrolled in 3 large trials. Patients were randomized to receive zoledronic acid, pamidronate, or placebo every 3-4 weeks for up to 24 months (prostate cancer, breast cancer, and multiple myeloma) or up to 21 months (lung and other solid tumors). RESULTS: A total of 3049 patients with multiple myeloma (n = 513), breast (n = 1130), prostate (n = 640), or lung cancer or other solid tumors (n = 766) were included in this analysis. Patients with multiple myeloma had the highest fracture incidence (43%), followed by breast (35%), prostate (19%), and lung cancer (17%). In all tumor types except lung, pathologic fracture was associated with a significant increase in risk of death, and breast cancer patients had the greatest increased risk. After adjustment for baseline characteristics, including performance status and prior skeletal complications, breast cancer patients who developed a pathologic fracture on study had a significant 32% increased risk of death relative to patients without a fracture (hazard ratio = 1.32; P < .01); patients with multiple myeloma or prostate cancer had a >20% increased risk of death. CONCLUSIONS: These results suggest that fractures are associated with increased risk of death in patients with malignant bone disease. Therefore, preventing fractures is an important goal of therapy.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".