Percutaneous sacroplasty for the management of painful pathologic fracture in a multiple myeloma patient: Case report and review of the literature
Bibliographic record
Abstract
Percutaneous kyphoplasty has a well-established role in the treatment of pathologic fractures in patients with multiple myeloma. Despite this, there is a scarcity of literature surrounding its use and efficacy in the sacrum. We present a case of successful symptom resolution in a patient with painful sacral fracture following sacroplasty, and review the existing literature. An 81-year-man with multiple myeloma presented to the hematology/oncology clinic with a history of excruciating pain while seated. The impact of this pain on his quality of life subjectively was rated to be particularly high. Computed tomography of the sacrum confirmed the presence of pathologic fracture within the S1 and S2 vertebrae. Under fluoroscopic guidance, polymethyl methacrylate (PMMA) bone cement was injected via 11-gauge needles using an anterior-oblique approach. No immediate post-procedural complications occurred, such as foraminal extravasation or venous injection. The patient reported himself to be pain-free 1 day following the procedure, and this remains the case to date at 2 years of follow-up. Sacroplasty is technically feasible and can provide durable relief of symptoms in patients with painful pathologic fractures of the sacrum. It is likely underused and can offer tremendous benefit to myeloma patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".