Considerations for a surgical RCT for diffuse low-grade glioma: a survey
Bibliographic record
Abstract
BACKGROUND: Diffuse low-grade gliomas (DLGGs) are heterogeneous tumors that inevitably differentiate into malignant entities, leading to disability and death. Recently, a shift toward up-front maximal safe resection of DLGGs has been favored. However, this transition is not supported by randomized controlled trial (RCT) data. Here, we sought to survey the neuro-oncology community on considerations for a surgical RCT for DLGGs. METHODS: A 21-question survey focusing on a surgical RCT for DLGGs was developed and validated by 2 neurosurgeons. A sample case of a patient for whom management might be debatable was presented to gather additional insight. The survey was disseminated to members of the Society for Neuro-Oncology (SNO) and responses were collected from March 16 to July 10, 2018. RESULTS: A total of 131 responses were collected. Sixty-three of 117 (54%) respondents thought an RCT would not be ethical, 39 of 117 (33%) would consider participating, and 56 of 117 (48%) believed an RCT would be valuable for determining the differing roles of biopsy, surgery, and observation. This was exemplified by an evenly distributed selection of the latter management options for our sample case. Eighty-three of 120 (69.2%) respondents did not believe in equipoise for DLGG patients. Quality of life and overall survival were deemed equally important end points for a putative RCT. CONCLUSIONS: Based on our survey, it is evident that management of certain DLGG patients is not well defined and an RCT may be justified. As with any surgical RCT, logistic challenges are anticipated. Robust patient-relevant end points and standardization of perioperative adjuncts are necessary if a surgical RCT is undertaken.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".