Hip arthroscopy for the management of osteoid osteoma of the acetabulum: a systematic review of the literature and case report
Bibliographic record
Abstract
BACKGROUND: Intra-articular osteoid osteoma (OO) causes irreversible joint damage. Its treatment of choice is radiofrequency ablation (RFA); however, some areas of the acetabulum are hard to access. Therefore, hip arthroscopy was used to treat this tumor. We aim to systematically review the literature with regards to arthroscopic management of acetabular OO, and to report a further case in which hip arthroscopy was used for treatment. METHODS: PubMed and EMBASE were searched for articles relevant to the arthroscopic management of acetabular OO on December 2, 2014. All articles published on and before that date were reviewed, and studies which met our pre-determined inclusion criteria were included. Articles screening and data abstraction were done by two reviewers independently. We also presented a 31-year-old man with acetabular OO who underwent hip arthroscopy for the management of his tumor after failing to respond to medications and computed tomography scan (CT)-guided RFA. RESULTS: The initial search revealed 14 studies, of which ten met our inclusion criteria. A total of ten patients underwent hip arthroscopy for the management of acetabular OO. Only two patients were females, and the patients' age ranged from 7 to 47 years. Two patients underwent arthroscopic guided-RFA of the lesion, while the rest underwent excision. The follow-up period ranged from 6 months to 2 years. Success rate was 100 %, and no recurrence was reported. Minor complications (transient impotence and perineal numbness) developed in one patient (10 %). Arthroscopic-guided RFA failed to eliminate the tumor in our additional case. A second trial of CT-guided RFA was successful in treating the patient's condition. CONCLUSIONS: Hip arthroscopy is an effective and safe option for the management of acetabular OO, with success rate exceeding 90 %. Studies of higher level of evidence are required.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| 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 | 0.000 |
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".