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Record W4392383953 · doi:10.1093/neuonc/noad225

Stereotactic body radiation therapy for spinal metastases: A new standard of care

2024· review· en· W4392383953 on OpenAlexaff
Amanda N. Sacino, Hanbo Chen, Arjun Sahgal, Chetan Bettegowda, Laurence D. Rhines, Pejman Maralani, Kristin J. Redmond

Bibliographic record

VenueNeuro-Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRadiosurgeryRadiation therapyStandard of careRadiologySurgery

Abstract

fetched live from OpenAlex

Advancements in systemic therapies for patients with metastatic cancer have improved overall survival and, hence, the number of patients living with spinal metastases. As a result, the need for more versatile and personalized treatments for spinal metastases to optimize long-term pain and local control has become increasingly important. Stereotactic body radiation therapy (SBRT) has been developed to meet this need by providing precise and conformal delivery of ablative high-dose-per-fraction radiation in few fractions while minimizing risk of toxicity. Additionally, advances in minimally invasive surgical techniques have also greatly improved care for patients with epidural disease and/or unstable spines, which may then be combined with SBRT for durable local control. In this review, we highlight the indications and controversies of SBRT along with new surgical techniques for the treatment of spinal metastases.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.070
GPT teacher head0.423
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations20
Published2024
Admission routes1
Has abstractyes

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