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Defining ablative radiotherapy in patients with hepatocellular carcinoma: An expert consensus.

2024· article· en· W4391090926 on OpenAlexaff
Ted K. Yanagihara, Joel E. Tepper, Andrew M. Moon, Aisling Barry, Meritxell Mollà, Jinsil Seong, Ferràn Torres, Theodore S. Lawrence, María Reig, Laura A. Dawson

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineAblative caseHepatocellular carcinomaExternal beam radiotherapyRadiation therapyDelphi methodNuclear medicineMedical physicsRadiologyBrachytherapyInternal medicine

Abstract

fetched live from OpenAlex

485 Background: External beam radiotherapy (EBRT) is a highly effective treatment in select patients with hepatocellular carcinoma (HCC). However, there are many types of EBRT described in the literature with no formal definition of what constitutes “ablative.” Thus, we convened a group of international experts to provide consensus on the parameters that define ablative EBRT in HCC. Methods: A Steering Committee was convened to generate a series of Key Criteria (KC) that could be used to define ablative EBRT for HCC. KC were based on factors related to dose, fractionation, radiobiology, target identification, and delivery technique. An international panel of experts participated in a modified Delphi (mDelphi) process to independently answer questions for each KC. Respondents were given 30 days to respond in round 1 of the mDelphi and 14 days to respond in round 2. A threshold of ≥ 70% was used to define consensus for answers to each KC. Results: Invitations were sent to 40 individuals and 35 (88%) returned responses. In the first round of the mDelphi, 3 of 7 KC reached consensus. In the second round, 100% of participants returned responses and consensus was reached in 3 of the remaining 4 KC. Based on this mDelphi analysis, there was expert agreement that ablative EBRT for HCC should be defined as 1) a BED10 ≥ 80 Gy, 2) daily imaging and multi-phasic contrast used for target delineation should be used, 3) treatment breaks (e.g., for adaptive EBRT) are allowed, but the total treatment time should be ≤ 6 weeks, 4) the equivalent dose when treating with protons should use a conversion factor of 1.1, and 5) there is no single conversion factor for carbon ions. In one KC, which questioned the expert’s opinion on the α/β ratio of HCC, consensus was not achieved. Conclusions: Using an mDelphi method assessing expert opinion, we provide the first consensus definition of ablative EBRT for HCC. Empiric data are required to define the α/β of HCC.

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)0.120
metaresearch head score (Gemma)0.112
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 score0.120
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0020.004
Open science0.0030.010
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.398
Teacher spread0.278 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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