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Record W4389919481 · doi:10.1016/j.ctro.2023.100716

Practice and principles of stereotactic body radiation therapy for spine and non-spine bone metastases

2023· article· en· W4389919481 on OpenAlexaff
Laura Burgess, Éric Nguyen, Chia‐Lin Tseng, Matthias Gückenberger, Simon S. Lo, Beibei Zhang, Michelle Nielsen, Pejman Maralani, Quynh‐Nhu Nguyen, Arjun Sahgal

Bibliographic record

VenueClinical and Translational Radiation Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRadiosurgeryRadiation therapyContouringRadiologyRandomized controlled trialClinical trialMedical physicsSurgery

Abstract

fetched live from OpenAlex

Radiotherapy is the dominant treatment modality for painful spine and non-spine bone metastases (NSBM). Historically, this was achieved with conventional low dose external beam radiotherapy, however, stereotactic body radiotherapy (SBRT) is increasingly applied for these indications. Meta-analyses and randomized clinical trials have demonstrated improved pain response and more durable tumor control with SBRT for spine metastases. However, in the setting of NSBM, there is limited evidence supporting global adoption and large scale randomized clinical trials are in need. SBRT is technically demanding requiring careful consideration of organ at risk tolerance, and strict adherence to technical requirements including immobilization, simulation, contouring and image-guidance procedures. Additional considerations include follow up practices after SBRT, with appropriate imaging playing a critical role in response assessment. Finally, there is renewed research into promising new technologies that may further refine the use of SBRT in both spinal and NSBM in the years to come.

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)0.026
metaresearch head score (Gemma)0.035
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: Methods · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.010
Scholarly communication0.0050.002
Open science0.0030.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0030.004

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 head0.079
GPT teacher head0.433
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
GenreMethods

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

Citations13
Published2023
Admission routes1
Has abstractyes

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