Vertebral Compression Fracture Treatment with Vertebroplasty and Kyphoplasty: Experience in 407 Patients with 1,156 Fractures in a Tertiary Cancer Center
Bibliographic record
Abstract
BACKGROUND: Painful vertebral compression fractures (VCFs), whether pathologic or osteoporotic, are a source of morbidity in cancer patients. At our tertiary cancer center, over the past decade we have used vertebroplasty (VP) and kyphoplasty (KP) to treat painful VCFs. More data are needed on the treatment of VCFs in cancer patients with these techniques. METHODS: We retrospectively reviewed the medical records of cancer patients with painful VCFs that had been treated at our institution between January 1, 2001 and May 31, 2008. Information was collected on demographic and clinical characteristics, features of the fractures, procedural details, and complications. Pre- and post-procedural pain and related symptoms were assessed using a subset of patients who had responded to the Brief Pain Inventory and the Edmonton Symptom Assessment Scale. RESULTS: A total of 407 cancer patients had 1,156 fractures that had been treated with VP or KP during 536 surgical procedures. Patients had an average of 2.8 fractures (range, 1-10). The majority of patients had pathologic fractures due to multiple myeloma (43%) or osteoporotic fractures (35%). Most fractures occurred in the thoracolumbar region. Adjacent-level fractures occurred in 18% of patients. Surgery provided significant relief from pain and several related symptoms. Symptomatic, serious complications requiring open surgery occurred in two cases (<0.01%) in our series. CONCLUSIONS: Our single-center experience revealed that a large number of cancer patients suffer from painful VCFs. The use of VP or KP in treating painful VCFs in cancer patients has good efficacy and an acceptably low complication rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".