EPID-16. CONSIDERATIONS FOR A SURGICAL RCT IN LOW-GRADE GLIOMAS: A SURVEY
Bibliographic record
Abstract
The management of diffuse low grade gliomas (LGGs) has seen a paradigmatic shift favoring maximal safe surgical resection (MSR). While this approach is not based on randomized-controlled trials (RCTs), the extent of evidence from observational data has prompted arguments against equipoise in LGG management, thus suggesting an RCT comparing MSR with other surgical options unethical. To explore opinions within the SNO neuro-oncology community regarding feasibility, ethics, and endpoints for a putative RCT comparing MSR with other management options. A survey of 19 questions was developed on the Qualtrics® platform and distributed to the SNO members on a one-time basis. Among 128 participants, 111 (87%) were consultants. The majority were neuro-oncologists (70, 55%), followed by neurosurgeons (41, 32%), and radiation oncologists (7, 6%). Thirty-five of 111 (32%) thought there was equipoise in LGG management. An RCT was thought to be ethical in 56/116 (48%) and potentially feasible in 66/108 (61%). Potential willingness to participate in an RCT was expressed by 73/108 (68%). There were no correlation between sub-specialty and any of the responses. The ideal patient for randomization was thought to be age < 40 years with minimal neurological symptoms presenting with a small (0-3cm) non-enhancing lesion in an eloquent/deep location. The ideal endpoint selected by most 39/97, 40% was the combination of overall survival and quality of life. The majority of respondents ruled out equipoise. However, LGGs are heterogeneous and an RCT may be needed to define the ideal management approach in a specific subset of this population. Quality of life and other patient-centered parameters must be incorporated as primary endpoints, alongside survival. Due to anticipated challenges such as a small sample size, heterogeneous management approaches, and a protracted clinical course, careful consideration of feasibility and a large-scale, multi-center approach would be necessary.
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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 | 0.179 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".