MétaCan
Menu
Back to cohort
Record W2089832745 · doi:10.1177/153303460800700603

Consistency Check of Planned Adaptive® Option on Helical Tomotherapy

2008· article· en· W2089832745 on OpenAlexaff
Maximilien R. P. Schirm, Slav Yartsev, Glenn Bauman, Jerry Battista, Jake Van Dyk

Bibliographic record

VenueTechnology in Cancer Research & Treatment · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsTomotherapyImaging phantomRadiation treatment planningMedicineNuclear medicineRadiation therapyRadiologyMedical physics

Abstract

fetched live from OpenAlex

This study aims to evaluate a new Planned Adaptive software (TomoTherapy Inc., Madison, WI) of the helical tomotherapy system by retrospective verification and adaptive re-planning of radiation treatment. Four patients with different disease sites (brain, nasal cavity, lungs, prostate) were planned in duplicate using the diagnostic planning kVCT data set and MVCT studies of the first treatment fraction with the same optimization parameters for both plan types. The dosimetric characteristics of minimum, maximum, and mean dose to the targets as well as to organs at risk were compared. Both sets of plans were used for calculation of dose distributions in a water-equivalent phantom. Corresponding measurements of these plans in phantom were carried out with the use of radiographic film and ion chamber. In the case of the lung and prostate cancer patients, changes in dosimetric parameters compared to data generated with the kVCT study alone were less than 2%. Certain changes for the nasal cavity and brain cancer patients were greater than 2%, but they were explained in part by anatomy changes that occurred during the time between kVCT and MVCT studies. The Planned Adaptive software allows for adaptive radiotherapy planning using the MVCT studies obtained by the helical tomotherapy imaging system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.095
GPT teacher head0.422
Teacher spread0.328 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueTechnology in Cancer Research & TreatmentSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207