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Record W3048384131 · doi:10.1002/pbc.28629

Practice patterns and recommendations for pediatric image‐guided radiotherapy: A Children's Oncology Group report

2020· article· en· W3048384131 on OpenAlexaff
Chia‐Ho Hua, T.Z. Vern-Gross, Clayton B. Hess, Arthur J. Olch, Parham Alaei, Vythialingam Sathiaseelan, Jun Deng, Kenneth Ulin, Fran Laurie, Mahesh Gopalakrishnan, Natia Esiashvili, Suzanne L. Wolden, Matthew J. Krasin, Thomas E. Merchant, Sarah S. Donaldson, Thomas J. FitzGerald, Louis S. Constine, Daphne A. Haas‐Kogan, Anita Mahajan, Nadia N. Laack, Karen J. Marcus, Paige A. Taylor, Verity Ahern, D Followill, Jeffrey C. Buchsbaum, John C. Breneman, John A. Kalapurakal

Bibliographic record

VenuePediatric Blood & Cancer · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedicineImage-guided radiation therapyRadiation oncologyMedical physicsPediatric oncologyCogModalitiesRadiation therapyModality (human–computer interaction)RadiologyInternal medicineCancerArtificial intelligence

Abstract

fetched live from OpenAlex

This report by the Radiation Oncology Discipline of Children's Oncology Group (COG) describes the practice patterns of pediatric image-guided radiotherapy (IGRT) based on a member survey and provides practice recommendations accordingly. The survey comprised of 11 vignettes asking clinicians about their recommended treatment modalities, IGRT preferences, and frequency of in-room verification. Technical questions asked physicists about imaging protocols, dose reduction, setup correction, and adaptive therapy. In this report, the COG Radiation Oncology Discipline provides an IGRT modality/frequency decision tree and the expert guidelines for the practice of ionizing image guidance in pediatric radiotherapy patients.

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)0.004
metaresearch head score (Gemma)0.026
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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