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Record W4324128872 · doi:10.2217/fon-2022-0893

Radiofrequency ablation versus stereotactic body radiation therapy for hepatocellular carcinoma: a meta-regression

2023· article· en· W4324128872 on OpenAlexaff
Aleena Malik, Meghan P. Jairam, Ronald Chow, Seyed Ali Mirshahvalad, Patrick Veit‐Haibach, Charles B. Simone

Bibliographic record

VenueFuture Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedicineRadiofrequency ablationHepatocellular carcinomaAblationRadiosurgeryMeta-analysisSample size determinationRadiation therapyRadiologyNuclear medicineInternal medicine

Abstract

fetched live from OpenAlex

Aim: The aim of this meta-regression was to assess the impact of mean/median age, mean/median tumor size, percentage of males in total sample, and total sample size on the comparative effectiveness of radiofrequency ablation (RFA) and stereotactic body radiation therapy (SBRT). Methods: Ten studies reporting on the composite outcome of overall survival and local control were included. Results: A significant relationship was found between age and overall survival at 1 and 2 for both RFA and SBRT. A significant relationship was noted also between age and local control at 1 and 2 years for RFA. Conclusion: Patients treated with SBRT had a wider range of tumor sizes and larger tumor sizes; no relationship was observed between tumor size and overall survival or local control by SBRT.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.120
GPT teacher head0.333
Teacher spread0.213 · 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 designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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