ACTR-28. A CALL FOR INCREASED ROLE OF NEUROSURGEONS IN SURGICAL TRIALS FOR NON-GLIOMATOUS PRIMARY CNS TUMORS: A SYSTEMATIC REVIEW OF THE CLINICALTRIALS.GOV DATABASE
Bibliographic record
Abstract
Abstract BACKGROUND Surgery continues to be part of the mainstay of therapy for most primary CNS tumors. However, neurosurgical innovations advancing treatment options have stagnated. An understanding of past and current investigational trends is necessary to catalyze innovation in surgical trials. OBJECTIVE To evaluate the landscape of surgical clinical trials for non-diffusely infiltrating primary CNS tumors. METHODS ClinicalTrials.gov was searched on May 14th, 2019 for surgical/procedural trials based on non-diffusely infiltrating primary CNS tumors, with a focus on meningiomas, ependymomas, medulloblastomas, pituitary adenomas, vestibular scwhannomas, and chordomas. Key trial design parameters were extracted. Purely surgical interventions were classified as “technical” while fluorescence-guided and other intraoperative imaging modalities were classified as image-guided surgery. Any invasive mode of drug delivery (e.g. intra-arterial or intra-ventricular, local chemotherapy wafers, and oncolytic virus injection) was considered. RESULTS Among 944 total trial records (257 medulloblastomas, 254 ependymomas, 179 meningiomas, 156 pituitary adenomas, 56 vestibular schwannomas, and 48 chordomas) only 54 (5.7%) were procedural in nature. Ependymomas had the highest absolute number (18/254, 7%) while vestibular schwannomas had the highest percentage (6/56, 10.7%) of surgical trials. The majority of records were not randomized (41/54, 75.9%), not industry-funded (45/54, 83.3%), and were early Phase (30/54, 55.6%); there were only three Phase III trials. The relative number of new surgical trial registrations has not changed significantly over time (1990s: 5, 2000s: 19, and 2010s: 30). Approaches integrated with other disciplines, such as image-guided surgery (16/54, 29.6%) and local drug delivery (16/24, 29.6%), were among the leading categories of trials. CONCLUSIONS Despite the clinical role of surgery in management of non-diffusely infiltrating primary CNS tumors, a paucity of innovative surgical trials has been put forth. Multi-disciplinary approaches appear to be necessary for pushing the therapeutic envelope for individuals affected by these tumors, with imaging and drug development at the forefront.
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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 | 0.073 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".