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Record W4380263572 · doi:10.21037/apm-23-218

A critical appraisal of the four systematic reviews and meta-analysis on stereotactic body radiation therapy versus external beam radiotherapy for painful bone metastases and where we go from here

2023· article· en· W4380263572 on OpenAlexaff
Henry C. Y. Wong, Adrian Wai Chan, Peter A.S. Johnstone, Charles B. Simone, Inmaculada Navarro-Domenech, Peter Hoskin, Candice Johnstone, Abram Recht, Johan Menten, Yvette M. van der Linden, Joanne M. van der Velden, Quynh‐Nhu Nguyen, Stephen Lutz, Nicolaus Andratschke, Jonas Wilmann, Joanna Kaźmierska, Mateusz Spałek, Fiona Mei-Ying Lim, Ya-Yu Tsai, B.A. Perez, Gustavo Nader Marta, Vassilios Vassiliou, Shing Fung Lee, Pierluigi Bonomo, Agata Rembielak, Edward Kai‐Hua Chow, Eva Oldenburger, Srinivas Raman

Bibliographic record

VenueAnnals of Palliative Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiation therapyCritical appraisalNuclear medicineSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Radiotherapy is an important treatment modality for pain control in patients with bone metastases. Stereotactic body radiation therapy (SBRT), which allows delivering a much higher dose per fraction while sparing critical structures compared to conventional external beam radiotherapy (cEBRT), has become more widely used, especially in the oligometastatic setting. Randomized controlled trials (RCTs) comparing the pain response rate of SBRT and cEBRT for bone metastases have shown conflicting results, as have four recent systematic reviews with meta-analyses of these trials. Possible reasons for the different outcomes between these reviews include differences in methodology, which trials were included, and the endpoints examined and how they were defined. We suggest ways to improve analysis of these RCTs, particularly performing an individual patient-level meta-analysis since the trials included heterogeneous populations. The results of such studies will help guide future investigations needed to validate patient selection criteria, optimize SBRT dose schedules, include additional endpoints (such as the time to onset of pain response, durability of pain response, quality of life (QOL), and side effects of SBRT), and better assess the cost-effectiveness and trade-offs of SBRT compared to cEBRT. An international Delphi consensus to guide selection of optimal candidates for SBRT is warranted before more prospective data is available.

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
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 head0.260
GPT teacher head0.453
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations5
Published2023
Admission routes1
Has abstractyes

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