AT-31A CANADIAN QUESTIONNAIRE ON THE MANAGEMENT OF ADULT DIFFUSE LOW GRADE GLIOMAS
Bibliographic record
Abstract
Adult diffuse low-grade gliomas (LGG) are slow growing, World Health Organization (WHO) grade II lesions with insidious onset and ultimate anaplastic transformation. The timing of surgery remains controversial with polarized practices continuing to govern patient management. As a result, the comprehensive management of these patients is variable. The goal of this questionnaire was to evaluate practice patterns in Canada. An online invitation for a questionnaire including diagnostic, preoperative, perioperative, and postoperative parameters and three cases with magnetic resonance imaging data with questions to various treatment options in these patients was sent to practicing neurosurgeons and trainees. 356 emails were sent with 87 (24.7%) responses collected. The range of years of practice was less then 10 years 36% (n = 23), 11-20 years 28% (n = 18), over 21 years 37% (n = 24). 90% of surgeons do not obtain formal preoperative neurocognitive assessments. 21% (n = 13) perform a biopsy upon first presentation. A gross total resection was believed to increase progression free survival (75%, n = 46) and to increase overall survival (64%, n = 39). Intraoperative MRI was only used by 8% of surgeons. Using the aid of three hypothetical cases with progressive complexities in tumor eloquence there was a trend for younger surgeons to operate earlier, and use awake craniotomy to obtain greater extent of resection with the aid of cortical stimulation when compared to senior surgeons who still more often preferred a “wait-and-see” approach. Despite the limitations of an online survey study, it provided insights into the variability in surgeon practice patterns in Canada and the need for a consensus on the workup and surgical management of this disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".