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Record W4387394574 · doi:10.3791/65504

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

2023· article· en· W4387394574 on OpenAlexaff
Laurence E. Court, Ajay Aggarwal, Hester Burger, Carlos Cárdenas, Christine Chung, Raphael Douglas, Monique du Toit, Anuja Jhingran, Raymond Mumme, Sikudhani Muya, Komeela Naidoo, Jerry Ndumbalo, Tucker Netherton, Callistus Nguyen, Adenike Olanrewaju, Jeannette Parkes, William V. Shaw, Christoph Trauernicht, Melody Xu, Jinzhong Yang, Lifei Zhang, Hannah Simonds, Beth M. Beadle

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsSt. Thomas Hospital
FundersNational Cancer InstituteCancer Prevention and Research Institute of TexasUniversity of Texas MD Anderson Cancer CenterWellcome TrustScience and Technology Facilities CouncilVarian Medical Systems
KeywordsComputer sciencePlan (archaeology)Radiation treatment planningUploadRadiation therapyService (business)Scale (ratio)Radiation oncologyMedical physicsWorld Wide WebMedicineRadiology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.457
Teacher spread0.422 · 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 designBench or experimental
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

Citations6
Published2023
Admission routes1
Has abstractyes

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