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Record W2009682197 · doi:10.1118/1.3475944

Special report: Workshop on 4D‐treatment planning in actively scanned particle therapy—Recommendations, technical challenges, and future research directions

2010· article· en· W2009682197 on OpenAlexaff
Antje Knopf, Christoph Bert, Emily Heath, Simeon Nill, Kim Melanie Kraus, Daniel Richter, Eugen B. Hug, Eros Pedroni, Sairos Safai, Francesca Albertini, Silvan Zenklusen, Dirk Boye, Matthias Söhn, Martin Soukup, B. Sobotta, Antony Lomax

Bibliographic record

VenueMedical Physics · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMedical physicsContext (archaeology)Session (web analytics)Radiation treatment planningPlenary sessionMedical physicistRadiation therapyMedicineComputer scienceLibrary scienceRadiology

Abstract

fetched live from OpenAlex

This article reports on a 4D-treatment planning workshop (4DTPW), held on 7-8 December 2009 at the Paul Scherrer Institut (PSI) in Villigen, Switzerland. The participants were all members of institutions actively involved in particle therapy delivery and research. The purpose of the 4DTPW was to discuss current approaches, challenges, and future research directions in 4D-treatment planning in the context of actively scanned particle radiotherapy. Key aspects were addressed in plenary sessions, in which leaders of the field summarized the state-of-the-art. Each plenary session was followed by an extensive discussion. As a result, this article presents a summary of recommendations for the treatment of mobile targets (intrafractional changes) with actively scanned particles and a list of requirements to elaborate and apply these guidelines clinically.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.397
Teacher spread0.334 · 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 designOther design
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

Citations52
Published2010
Admission routes1
Has abstractyes

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