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Record W4391036180 · doi:10.1016/j.ctro.2024.100728

Lessons learned for spine SABR?

2024· article· en· W4391036180 on OpenAlexaffabout
Stéphane Thibodeau, Gregory Paulin, Fábio Ynoe de Moraes

Bibliographic record

VenueClinical and Translational Radiation Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsSABR volatility modelMedicineRandomized controlled trialMedical physicsSurgery

Abstract

fetched live from OpenAlex

•SABR has shown survival benefits in oligometastatic cases, particularly in low-volume metastatic disease states.•Spine SABR offers potential improvements in local control, and pain response for metastatic spine tumors.•Technical requirements for SABR, like advanced image guidance and immobilization systems, are increasingly available.•Current data suggests high local control rates (75-95%) and variable pain responses (40-90%) with SABR using single or multi-fraction regimens.•Further randomized trials are needed to explore SABR applications in different scenarios and populations, while considering global health aspects for wider accessibility.

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)0.011
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0040.011
Open science0.0040.002
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0220.008

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 head0.175
GPT teacher head0.515
Teacher spread0.340 · 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 designObservational
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

Citations0
Published2024
Admission routes2
Has abstractyes

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