Percutaneous Radiofrequency Ablation of Osteoid Osteoma
Bibliographic record
Abstract
Osteoid osteoma is a benign bone tumor. Patients usually require surgical treatment for reliable pain relief. Difficulties with intraoperative localization of the tumor and anatomic locations that carry a high morbidity with en bloc resection complicate open surgery. Various methods have been developed to lessen the invasiveness of surgery including computed tomography-guided percutaneous radiofrequency thermal ablation. Eleven patients in three different centers were evaluated and diagnosed with osteoid osteoma based on typical histories, physical examinations, and imaging studies. All patients were treated with computed tomography-guided percutaneous radiofrequency thermal ablation after medical treatment failed. Excellent pain relief was reported in 10 patients. One patient suffered recurrence of a femoral neck lesion despite an initial 7-month period without pain. Patients were given a questionnaire to quantify the effectiveness of percutaneous radiofrequency ablation in terms of pain relief and return to function. The current study shows that percutaneous radiofrequency thermal ablation provides reliable, excellent pain relief and early return to function with minimal morbidity as compared with traditional open techniques. The authors suggest that this technique be used for all patients with extraspinal osteoid osteomas that are not immediately adjacent to neurovascular structures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".