Minimally Invasive Resection of an S3 Osteoid Osteoma Using an Intraoperative O-Arm: A Technical Note
Bibliographic record
Abstract
Osteoid osteomas are benign primary bone tumors that typically arise in posterior vertebrae of the spine. For patients with severe pain or those poorly controlled with non-steroidal anti-inflammatory drugs, surgical management is the mainstay of treatment. The recommended surgical treatment option is complete open excision, although minimally invasive CT-guided percutaneous excision and CT-guided radiofrequency ablation have been reported. Open resection can result in prolonged hospital stays, activity restrictions, and possible spinal destabilization. We sought to utilize a lateral minimally invasive approach. We highlight the importance of aggressive surgical resection and the utility of using fluoroscopy and O-arm guidance to optimize the extent of resection. We report a pediatric case of a 12-year-old male who presented with an S3 osteoid osteoma. The patient underwent a minimally invasive resection with complete resection and confirmation of the histopathologic diagnosis. Postoperative imaging showed complete resection of the tumor. The patient went home five hours after surgery with return to daily activities; his symptoms resolved completely. However, the patient had symptomatic recurrence and underwent a second more aggressive minimally invasive resection using O-arm guidance. At the current three-month follow-up, the patient is symptom- and tumor-free. The minimally invasive resection of a pediatric sacral osteoid osteoma is a valid alternative to standard open resection and is associated with a decreased blood loss, decreased length of stay in the hospital, and decreased time to full functional recovery. The pitfalls are learning curve and risk of incomplete resection that can be counterbalanced with an intraoperative O-arm to guide resection and confirm complete excision.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".