Osteosarcoma of the spine: prognostic variables for local recurrence and overall survival, a multicenter ambispective study
Bibliographic record
Abstract
OBJECTIVE Primary spinal osteosarcomas are rare and aggressive neoplasms. Poor outcomes can occur, as obtaining marginal margins is technically demanding; further Enneking-appropriate en bloc resection can have significant morbidity. The goal of this study is to identify prognostic variables for local recurrence and mortality in surgically treated patients diagnosed with a primary osteosarcoma of the spine. METHODS A multicenter ambispective database of surgically treated patients with primary spine osteosarcomas was developed by AOSpine Knowledge Forum Tumor. Patient demographic, diagnosis, treatment, perioperative morbidity, local recurrence, and cross-sectional survival data were collected. Tumors were classified in 2 cohorts: Enneking appropriate (EA) and Enneking inappropriate (EI), as defined by pathology margin matching Enneking-recommended surgical margins. Prognostic variables were analyzed in reference to local recurrence and survival. RESULTS Between 1987 and 2012, 58 patients (32 female patients) underwent surgical treatment for primary spinal osteosarcoma. Patients were followed for a mean period of 3.5 ± 3.5 years (range 0.5 days to 14.3 years). The median survival for the entire cohort was 6.7 years postoperative. Twenty-four (41%) patients died, and 17 (30%) patients suffered a local recurrence, 10 (59%) of whom died. Twenty-nine (53%) patients underwent EA resection while 26 (47%) patients underwent EI resection with a postoperative median survival of 6.8 and 3.7 years, respectively (p = 0.048). EI patients had a higher rate of local recurrence than EA patients (p = 0.001). Patient age, previous surgery, biopsy type, tumor size, spine level, and chemotherapy timing did not significantly influence recurrence and survival. CONCLUSIONS Osteosarcoma of the spine presents a significant challenge, and most patients die in spite of aggressive surgery. There is a significant decrease in recurrence and an increase in survival with en bloc resection (EA) when compared with intralesional resection (EI). The effect of adjuvant and neoadjuvant chemotherapeutics, as well as method of biopsy, requires further exploration.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".