Volumetric quantification of cement leakage following percutaneous vertebroplasty in metastatic and osteoporotic vertebrae
Bibliographic record
Abstract
OBJECT: The goal of this study was to quantify volumetrically cement fill and leakage in patients with osteoporotic and metastatic vertebral lesions undergoing percutaneous vertebroplasty and to establish whether these factors have any clinical significance at follow up. METHODS: Digital computerized tomography data were retrospectively collected from all cases at the authors' institution in which percutaneous vertebroplasty was performed for osteoporosis or metastatic disease. Patient selection was based on the consensus of a multidisciplinary team consisting of an orthopedic surgeon, an oncologist, and a neuroradiologist. A semiautomated thresholding technique was used to measure vertebral body volume, the volume of cement injected directly into the vertebra, and the volume of cement leakage. Pain-related scores were collected at four early stages of treatment, and all clinical complications were recorded. Cement leakage was found in 87.9% of vertebrae treated with percutaneous vertebroplasty. In osteoporotic vertebrae it occurred mainly in the disc, whereas in metastatic lesions, it was found in multiple areas. Irrespective of leakage, both patients with osteoporotic and metastatic disease experienced significant immediate pain relief postoperatively. CONCLUSIONS: Although there was no correlation between cement fill or cement leakage and pain relief, there exists a risk of serious complications due to cement leakage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".