Recent advances and new discoveries in the pipeline of the treatment of primary spinal tumors and spinal metastases: a scoping review of registered clinical studies from 2000 to 2020
Bibliographic record
Abstract
The field of spinal oncology has substantially evolved over the past decades. This review synthesizes and appraises what was learned and what will potentially be discovered from the recently completed and ongoing clinical studies related to the treatment of primary and secondary spinal neoplasms. This scoping review included all clinical studies on the treatment of spinal neoplasms registered in the ClinicalTrials.gov website from February 2000 to December 2020. The terms "spinal cord tumor," "spinal metastasis," and "metastatic spinal cord compression" were used. Of the 174 registered clinical studies on primary spinal tumors and spinal metastasis, most of the clinical studies registered in this American registry were interventional studies led by single institutions in North America (n = 101), Europe (n = 43), Asia (n = 24), or other continents (n = 6). The registered clinical studies mainly focused on treatment strategies for spinal neoplasms (90.2%) that included investigating stereotactic radiosurgery (n = 33), radiotherapy (n = 21), chemotherapy (n = 20), and surgical technique (n = 11). Of the 69 completed studies, the results from 44 studies were published in the literature. In conclusion, this review highlights the key features of the 174 clinical studies on spinal neoplasms that were registered from 2000 to 2020. Clinical trials were heavily skewed toward the metastatic population as opposed to the primary tumors which likely reflects the rarity of the latter condition and associated challenges in undertaking prospective clinical studies in this population. This review serves to emphasize the need for a focused approach to enhancing translational research in spinal neoplasms with a particular emphasis on primary tumors.
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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 | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".