A pilot survey into the landscape of neuro-oncology care in the community
Bibliographic record
Abstract
BACKGROUND: The complexities of the field of neuro-oncology require multidisciplinary collaboration in order to deliver contemporary comprehensive care. There is increasing awareness that much of neuro-oncology care occurs in the community setting. In 2022, the Society for Neuro-Oncology (SNO) created the Community Neuro-Oncology Committee (CNO) in an inaugural attempt to formally acknowledge community neuro-oncology practitioners. METHODS: A 19 question survey was developed by SNO-CNO to gather initial data on the current landscape of neuro-oncology care in the community. The survey was distributed via the SNO newsletter and email blasts as well as through partnerships with multiple advocacy groups. Results were analyzed and tabulated through R2. RESULTS: There were 112 responses from providers in the United States and Canada. Most providers were physicians and represented multiple disciplines including neurology, neuro-oncology, medical oncology, neurosurgery, and radiation oncology. Sixty-four (57%) described themselves as neuro-oncology-focused. Eighty-eight (79%) reported access to neuro-oncology tumor boards. Sixty-eight (73%) stated they had access to molecular tumor boards. Most respondents felt that they were adequately supported to manage neuro-oncology patients. When dividing responses based on a neuro-oncology-focused practice compared to a less neuro-oncology-focused practice, there were significant differences between access to molecular tumors boards (85% vs 63%, P = .023) and access to clinical trials (98% vs 82%, P = .022). CONCLUSION: This qualitative and quantitative hypothesis-generating data is the start of understanding the challenges faced by community neuro-oncology providers. These results will guide future studies and recommendations aimed toward better supporting them and their patients.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".