MétaCan
Menu
Back to cohort
Record W2901540609 · doi:10.1002/mp.12679

Motion management in particle therapy

2018· review· en· W2901540609 on OpenAlexaff
Shinichiro Mori, Antje Knopf, Kikuo Umegaki

Bibliographic record

VenueMedical Physics · 2018
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsMotion (physics)Particle therapyTracking (education)Match movingComputer scienceMedical physicsMedicineComputer visionRadiologyRadiation therapy

Abstract

fetched live from OpenAlex

In this review article, we introduced the importance of "motion management" in advanced particle therapy. Several publications have reported that organ motion causes dose distribution disturbances due to interplay and blurring effects. Furthermore, motion can result in target dose miss and unwanted dose to healthy structures around the target. To avoid these problems, motion should be assessed and monitored prior and during treatment. In this review article, we give an overview about clinically available motion monitoring systems. Based on the acquired motion information an adequate motion mitigation technique should be chosen. This article reviews the clinical status of motion mitigation techniques like rescanning, gating and tracking. A limited number of centers have now started the treatment of targets in the thorax and abdomen using scanned particle beams. Therefore, the establishment of guidelines for motion monitoring and motion mitigation will be essential in the coming years.

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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.037
GPT teacher head0.366
Teacher spread0.329 · 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
GenreReview

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

Citations57
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207