Perfecting the pour: A novel co-axial technique with sequential injections for optimising cement delivery during sacroplasty
Bibliographic record
Abstract
Background Percutaneous sacroplasty is an effective treatment for painful sacral fractures and tumours, however there is no accepted optimal technique for performing this procedure. This study investigated a novel approach to sacroplasty combining co-axial sacral access, sequential cement injections and hypothermic cement manipulation to improve cement delivery. Methods This retrospective study analysed 11 patients who underwent co-axial sacroplasty between April 2023 and March 2024 for treatment of painful insufficiency fractures ( n = 5) or malignant sacral tumours ( n = 6). All cases were performed using biplane fluoroscopy with conebeam CT navigation for planning and monitoring percutaneous access. Procedural details, technical outcomes, and clinical outcomes including Numerical Rating Scale (NRS) pain and analgesic utilisation on a six-point scale were analysed pre-procedure and at follow-up. Results Technical success of was achieved in all cases using this technique. The mean injected cement volume was 20.5 ± 6.4 ml. Median pre-procedural NRS pain scores of 8 (IQR 7.25–8) significantly decreased to 0 (IQR, 0–0.25) at follow-up ( p <.01). The median preprocedural analgesic utilisation score reduced from 3 (IQR, 2–3) to 0 (IQR, 0–2.5) at follow-up ( p <.01). Cement leakage occurred during two cases without associated adverse clinical sequelae. There were no major adverse events. Conclusion Co-axial sequential injection sacroplasty is a safe and effective technique which allows facilitates controlled delivery of cement. Improved control of cement delivery, including around high-risk structures for cement leakage, offers a potential safety advantage over conventional sacroplasty techniques. Further research comparing technical and clinical outcomes to conventional techniques is warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".