NCMP-09. THE CURRENT LANDSCAPE OF SPINE ONCOLOGY SURGICAL TRIALS AND FUTURE OPPORTUNITIES
Bibliographic record
Abstract
Abstract BACKGROUND Clinical trials have allowed the discovery of successful therapeutic intervention for cancer patients. The current number of cancer patients in the US has increased with an incidence of 442/100,000 population and yet with decreased yearly mortality rate. Spine metastatic disease occurs in approximately 90% of patients with cancer. The aim of this study is to analyze the current landscape of spine oncology trials to identify future opportunities. METHODS To assess active and ongoing clinical trials in the treatment of metastatic spinal disease, the authors queried https://clinicaltrials.gov/ under the searches “Spine Metastases” and “Spine Tumor.” Only clinical trials designated as “Recruiting” were included. Parametric and non-parametric statistical analysis was performed. RESULTS Our initial search yielded to 100 total studies; 18 clinical trials met entry criteria. Trial type was 16/18 therapeutic, 2/18 observational and trial design 6/18 (33%) randomized. All 18 trials were single center. Trials were were based in the United States (90%) and British Columbia, Canada. Across US geographical quadrants, a significant higher number of therapeutic trials was found in the South quadrant (12/16) compared to the other quadrants, (p< 0.001; Midwest: N=3/16, Northeast: N=1). The most common therapeutic focus included radiosurgery, followed by vertebral body augmentation and/or radiofrequency ablation, and laser interstitial thermal therapy (LITT). CONCLUSION Clinical trials are a cornerstone for the advancement oncological treatment. Our study shows that currently there is an ample opportunity to design clinical trials for our growing population of metastatic spine disease patients. Successful strategies to enhance the needed portfolio of spinal oncology trials, include focusing on including all US geographic quadrants. As new trials are designed, continued efforts devoted to data entry in national registry remains important.
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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 | 0.216 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".