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Record W4391269895 · doi:10.3171/2023.12.jns232781

The AANS/CNS Section on Tumors: a summary of 40 years of advocacy to advance the care of patients with brain and spine tumors

2024· article· en· W4391269895 on OpenAlex
Ian F. Parney, Ronald E. Warnick, Frederick F. Lang, James T. Rutka, Steven N. Kalkanis, Roberta P. Glick, Mark L. Rosenblum, Isabelle M. Germano

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of neurosurgery · 2024
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSection (typography)OncologySurgical oncologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.262
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it