The AANS/CNS Section on Tumors: a summary of 40 years of advocacy to advance the care of patients with brain and spine tumors
Bibliographic record
Abstract
The AANS/CNS Section on Tumors was founded 40 years ago in 1984 to assist in the education of neurosurgeons interested in neuro-oncology, and serves as a resource for other national organizations regarding the clinical treatment of nervous system tumors. The Section on Tumors was the first national physicians' professional organization dedicated to the study and treatment of patients with brain and spine tumors. Over the past 40 years, the Section on Tumors has built solid foundations, including establishing the tumor section satellite meetings, founding the Journal of Neuro-Oncology (the first medical journal dedicated to brain and spine surgical oncology), advancing surgical neuro-oncology education and research, promoting neurosurgical involvement in neuro-oncology clinical trials, and advocating for patients with brain and spine tumors. This review provides a synopsis of the Section on Tumors' history, its challenges, and its opportunities, drawing on the section's archives and input from the 17 section chairs who led it during its first 40 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".