The effect of bone cement distribution on clinical efficacy after percutaneous kyphoplasty for osteoporotic vertebral compression fractures
Bibliographic record
Abstract
To evaluate the influence of various distributions of bone cement on the clinical efficacy of percutaneous kyphoplasty (PKP) in treating osteoporotic vertebrae compression fractures.A total of 201 OVCF patients (30 males and 171 females) who received PKP treatment in our hospital were enrolled in this study. According to the characteristic of cement distribution, patients were divided into 2 groups: group A ("H" shaped group), the filling pattern in vertebral body were 2 briquettes and connected with / without cement bridge; and group B ("O" shaped group), the filling pattern in vertebral body was a complete crumb and without any separation. Bone mineral density, volume of injected cement, radiographic parameters, and VAS scores were recorded and analyzed between the 2 groups.All patients finished at least a 1-year follow-up and both groups had significant improvement in radiographic parameters and clinical results. No significant differences in BMD, operation time, bleeding volume, or leakage of cement were observed between the 2 groups. Compared with group B, group A had a larger use of bone cement, lower proportion of unipedicular approach, and better VAS scores at 1 year after surgery.Both "H" and "O" shaped distribution pattern can improve radiographic data and clinical outcomes effectively. However, "H" shaped distribution can achieve better clinical recovery at short-term follow-up.
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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) | 0.000 | 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 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".