Benign Tumors of the Spine
Bibliographic record
Abstract
STUDY DESIGN: Clinically based systematic review. OBJECTIVE: To determine the role of (A) medical treatment and (B) interventional radiology as either adjuvant or stand-alone treatment in primary benign bone tumors of the spine. METHODS: A multidisciplinary panel of spine surgeons, radiation oncologists, and medical oncologists elaborated specific focused questions regarding aneurysmal bone cyst, giant cell tumor, and osteoid osteoma. Denosumab, bisphosphonate, interferon, bone marrow aspirate, doxycycline, thermal ablation, and selective arterial embolization were identified as areas of interest for the article. A systematic review was performed through MEDLINE and EMBASE. Recommendations based on the literature review and clinical expertise were issued using the GRADE system. RESULTS: The overall quality of the literature is very low with few multicenter prospective studies. For giant cell tumor, combination with Denosumab identified 14 pertinent articles with four multicenter prospective studies. Nine studies were found on bisphosphonates and six for selective arterial embolization. The search on aneurysmal bone cyst and selective arterial embolization revealed 12 articles. Combination with Denosumab, Doxycycline, and bone marrow aspirate identified four, two, and three relevant articles respectively. Eleven focused articles were selected on the role of thermal ablation in osteoid osteoma. CONCLUSION: Alternative and adjuvant therapy for primary benign bone tumors have emerged. Their ability to complement or replace surgery is now being scrutinized and they may impact significantly the algorithm of treatment of these tumors. Most of the data are still emerging and further research is desirable. Close collaboration between the different specialists managing these pathologies is crucial. LEVEL OF EVIDENCE: N/A.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".