Bibliographic record
Abstract
BACKGROUND: Though vertebroplasty is a well-known and extremely effective procedure in experienced hands, it is a much more difficult procedure to learn than standard spinal pain injection procedures. We therefore present a simplified, methodical approach to vertebroplasty that can be adopted by trained interventionalists. Many practitioners who attend hands-on cadaver workshops lack confidence to apply this technique in live patients. OBJECTIVES: To present a methodical, reproducible, and proven technique. To provide strategies on pre-procedure and post-procedure care in order to optimize outcomes in vertebroplasty patients. STUDY DESIGN: A step-by-step tutorial is presented outlining the steps in the vertebroplasty procedure. A discussion of anatomic considerations, pre-procedure patient selection issues, and post-procedure management is also presented. METHODS: Sections are presented on anatomy, patient selection, a 10-step technique on performance of vertebroplasty, a discussion of how this technique is advantageous, and post-procedure management. RESULTS: This technique has been proven in clinical practice for over 1,500 vertebroplasties and has been well-received the past 4 years by hundreds of trainees taught at numerous hands-on courses (Stryker Interventional Pain, Arthrocare, and Society of Interventional Radiology). CONCLUSION: A basic tutorial is presented for the beginner who is interested in vertebroplasty. This safe and reproducible technique has been proven in clinical practice. The anatomic considerations, patient selection issues, technique, and post-procedure management has been taught and well received by hundreds of physicians at numerous hands on courses within the United States and Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".