A practical guide for planning pelvic bone percutaneous interventions (biopsy, tumour ablation and cementoplasty)
Bibliographic record
Abstract
Percutaneous approaches for pelvic bone procedures (bone biopsies, tumour ablation and cementoplasty) are multiple and less well systematised than for the spine or extremities. Among the different imaging techniques that can be used for guidance, computed tomography (CT) scan is the modality of choice because of the complex pelvic anatomy. In specific cases, such as cementoplasty where real-time evaluation is a determinant, a combination of CT and fluoroscopy is highly recommended. The objective of this article is to propose a systematic approach for image-guided pelvic bone procedures, as well as to provide some technical tips. We illustrate the article with multiple examples, and diagrams of the approaches and important structures to avoid to perform these procedures safely. TEACHING POINTS: • Pelvic bone procedures are safe to perform if anatomical landmarks are recognised. • The safest approach varies depending on the pelvic level. • CT is the modality of choice for guiding pelvic percutaneous procedures. • Fluoroscopy is recommended when real-time monitoring is mandatory. • MRI can also be used for guiding pelvic percutaneous procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.030 |
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".