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Record W2950177304 · doi:10.1093/neuonc/noz014

Updates in the management of intradural spinal cord tumors: a radiation oncology focus

2019· review· en· W2950177304 on OpenAlexaff
Rupesh Kotecha, Minesh P. Mehta, Eric L. Chang, Paul D. Brown, John H. Suh, Simon S. Lo, Sunit Das, Haider Samawi, Julia Keith, James Perry, Arjun Sahgal

Bibliographic record

VenueNeuro-Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsMedicineRadiosurgerySpinal cordRadiation therapyAdjuvant radiotherapyRadiation treatment planningNeuroimagingRadiation oncologyRadiology

Abstract

fetched live from OpenAlex

Primary spinal cord tumors represent a hetereogeneous group of central nervous system malignancies whose management is complex given the relatively uncommon nature of the disease and variety of tumor subtypes, functional neurologic deficits from the tumor, and potential morbidities associated with definitive treatment. Advances in neuroimaging; integration of diagnostic, prognostic, and predictive molecular testing into tumor classification; and developments in neurosurgical techniques have refined the current role of radiotherapy in the multimodal management of patients with primary spinal cord tumors, and corroborated the need for prospective, multidisciplinary discussion and treatment decision making. Radiotherapeutic technological advances have dramatically improved the entire continuum from treatment planning to treatment delivery, and the development of stereotactic radiosurgery and proton radiotherapy provides new radiotherapy options for patients treated in the definitive, adjuvant, or salvage setting. The objective of this comprehensive review is to provide a contemporary overview of the management of primary intradural spinal cord tumors, with a focus on radiotherapy.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.061
GPT teacher head0.390
Teacher spread0.329 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations45
Published2019
Admission routes1
Has abstractyes

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