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Record W2014701386 · doi:10.1088/0031-9155/49/5/006

Dosimetry of<i>in situ</i>activated dysprosium microspheres

2004· article· en· W2014701386 on OpenAlexafffund
N. Adnani

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsAlberta Medical Association
FundersUniversity of Alberta
KeywordsDysprosiumCollimatorMaterials scienceDosimetryNuclear medicineImaging phantomPhotonLinear particle acceleratorRadiochemistryBeam (structure)OpticsPhysicsChemistryNuclear physicsMedicine

Abstract

fetched live from OpenAlex

This paper presents the results of a study aimed at investigating the dosimetry of stable dysprosium microspheres activated, in situ, by a linac generated photon beam. In phantom measurements of the neutron flux within an 18 MV photon beam were performed using CR-39 detectors and gold activation. The results were used in conjunction with a Monte Carlo computer simulation to investigate the dose distribution resulting from the activation of dysprosium (Dy) microspheres using an 18 MV photon beam. Different depths, lesion volumes and volume concentrations of microspheres are investigated. The linac lower collimator jaws are assumed completely closed to shield the tumour volume from the photon dose. Using a single AP field with 0 x 0 cm2 field size (closed jaws), a photon dose rate of 600 MU min(-1) and 80 cm SSD for 10 min, an average dose exceeding 1 Gy can be delivered to spherical lesions of 0.5 cm and higher diameter. The variation of the average dose with the size of the lesion reaches saturation for tumour volumes exceeding 1 cm in diameter. This report shows that the photon beam of a high-energy linac can be used to activate Dy microspheres in situ and, as a result, deliver a significant dose of beta radiation. Non-radioactive Dy microspheres do not have the toxicity and imaging problems associated with commercially available yttrium-90 based products.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.251

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 head0.065
GPT teacher head0.335
Teacher spread0.270 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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