MétaCan
Menu
Back to cohort
Record W1802737731 · doi:10.1088/0031-9155/60/11/n231

3D printed plastics for beam modulation in proton therapy

2015· article· en· W1802737731 on OpenAlexafffund
Clay Lindsay, Joel Kumlin, Andrew Jirasek, R Lee, D. Mark Martinez, Paul Schaffer, Cornelia Hoehr

Bibliographic record

VenuePhysics in Medicine and Biology · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British ColumbiaUniversity of VictoriaBC Cancer AgencyTRIUMF
FundersTRIUMF
KeywordsMaterials scienceFused filament fabricationFabricationStopping power3d printedProton therapyBeam (structure)ProtonPower densityOptics3D printingComposite materialBiomedical engineeringPower (physics)Nuclear physicsPhysicsDetector

Abstract

fetched live from OpenAlex

Two 3D printing methods, fused filament fabrication (FFF) and PolyJet™ (PJ) were investigated for suitability in clinical proton therapy (PT) energy modulation. Measurements of printing precision, printed density and mean stopping power are presented. FFF is found to be accurate to 0.1 mm, to contain a void fraction of 13% due to air pockets and to have a mean stopping power dependent on geometry. PJ was found to print accurate to 0.05 mm, with a material density and mean stopping power consistent with solid poly(methyl methacrylate) (PMMA). Both FFF and PJ were found to print significant, sporadic defects associated with sharp edges on the order of 0.2 mm. Site standard PT modulator wheels were printed using both methods. Measured depth-dose profiles with a 74 MeV beam show poor agreement between PMMA and printed FFF wheels. PJ printed wheel depth-dose agreed with PMMA within 1% of treatment dose except for a distal falloff discrepancy of 0.5 mm.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.210

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 integrity0.0000.000
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 head0.307
GPT teacher head0.435
Teacher spread0.128 · 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 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

Citations24
Published2015
Admission routes2
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicRadiation Therapy and DosimetryFrench-language works237,207