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Record W4400731808 · doi:10.1093/noajnl/vdae067

Current state of spinal nerve sheath tumor management and future advances

2024· review· en· W4400731808 on OpenAlex
Chloe Gui, Luxshikka Canthiya, Gelareh Zadeh, Suganth Suppiah

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

VenueNeuro-Oncology Advances · 2024
Typereview
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurrent (fluid)State (computer science)MedicineNeuroscienceComputer scienceEngineeringPsychologyElectrical engineering

Abstract

fetched live from OpenAlex

Nerve sheath tumors are the most common tumors of the spine after meningiomas. They include schwannomas, neurofibroma, and malignant peripheral nerve sheath tumors. These can arise sporadically or in association with tumor predisposition syndromes, including neurofibromatosis type 1, neurofibromatosis type 2, and schwannomatosis. Though surgery is the traditional mainstay of treatment for these tumors, the discovery of the genetic and molecular basis of these diseases in recent decades has prompted investigation into targeted therapies. Here, we give a clinical overview of spinal nerve sheath tumors, their imaging features, current management practices, and explore ongoing advances in systemic therapies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.369
Teacher spread0.339 · 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