Surgical Management of Spinal Osteoblastomas
Bibliographic record
Abstract
Introduction Primary spinal osteoblastomas are rare benign neoplasms which often behave more aggressively than other benign tumors and can present as malignant transformations. Optimal surgical treatment strategies and risk factors for local recurrence and mortality of spinal osteoblastomas remain unclear. The aim of this multicenter cohort study was to assess rates of local recurrence and mortality following surgical intervention for spinal osteoblastomas and to evaluate whether the application of the Enneking classification in the management of these tumors influences local recurrence and mortality. Methods The AOSpine Knowledge Forum Tumor developed a multicenter ambispective database of patients who underwent surgical intervention for spinal osteoblastoma. Patient demographic, diagnosis, treatment, cross-sectional survival, and local recurrence data were collected. Patients were analyzed in two cohorts based on the Enneking classification of the tumor: Enneking appropriate (EA) and Enneking inappropriate (EI). EA was defined by the final pathology margin matching the Enneking recommended surgical margin, if otherwise, it was defined as EI. Results Between November 1991 and June 2012, a total of 102 patients diagnosed with a spinal osteoblastoma were identified. Twenty-eight patients were omitted from the analysis due to insufficient follow-up (<12 months) or incomplete survival data, leaving 74 patients for final analysis. The mean follow-up was 4.3 ± 2.8 years in the EI and 4.5 ± 3.3 years in the EA group. Thirteen (18%) patients suffered a local recurrence and six (8%) patients died during the study period. Local recurrence was strongly associated with mortality with a relative risk of 9.4 ( p = 0.007). When adjusting for Enneking appropriateness, the result was not significantly altered. Significant differences were not found between the EA and EI groups for local recurrence and mortality. Conclusion Upon evaluating the largest multicenter cohort of spinal osteoblastomas to date, the application of the Enneking classification as treatment guide for spinal osteoblastomas could not be confirmed. Considering the consequences of a local recurrence and the strong association of local recurrence with mortality even after adjusting for Enneking appropriateness, en bloc or marginal resection is nevertheless the recommended surgical treatment strategy for spinal osteoblastoma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".