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Record W1968436591 · doi:10.1118/1.2962031

SU‐GG‐T‐279: Evaluation of a Novel 4D In‐Vivo Dosimeter

2008· article· en· W1968436591 on OpenAlexaff
Amanda Cherpak, A Hallil, W. Ding, Joanna Cygler

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsDosimeterDetectorMaterials scienceSIGNAL (programming language)BrassOpticsNuclear medicineQuality assuranceDosimetryBiomedical engineeringAnalytical Chemistry (journal)PhysicsChemistryMedicineCopper

Abstract

fetched live from OpenAlex

Purpose: To present the latest quality assurance and characterization tests of the RADPOS 4‐D in vivo dosimetry system. Method and Materials: The dosimetric evaluation of this system included the measurement of in‐air dose profiles in , 6 MV, and 18 MV beams, and the investigation of the dependence of detector response on beam angle and field size. The stability and accuracy of the positioning component of the RADPOS detector was studied as well as the effect of metals and other commonly used materials on the RADPOS signal. Results: The dose profiles measured with the RADPOS detector and the diode agreed in within 0.41%, 0.53%, and 2.69% for the , 6 MV, and 18 MV beams, respectively. The angular response of the RADPOS probe over 360° was isotropic within 1.6% (1SD). Over a period of seventy minutes, the position of the RADPOS was read every 30 s and found stable within 0.37 mm. The system can also measure the displacement of a RADPOS detector with an accuracy of (0.45 ± 0.07) mm and (0.75 ± 0.07) mm for step sizes up to 50 mm and 200 mm respectively. The only materials that caused significant interference with the RADPOS signal were aluminum, brass, steel and lead. However, once the separation between the detector and the sample was >100 mm, the interference was minimal (the average deviation was less than 1.00 mm for all samples and sizes). Conclusion: Results of the preliminary tests indicate that the device can be used for in‐vivo dosimetry in and high‐energy beams from linear accelerators. Future work will involve technical improvements to the device, experiments in a 4D phantom and finally patient in‐vivo dosimetry. Acknowledgement: This project has been supported by a grant from HTX‐OCE‐IRAP and Best Medical.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.332
Teacher spread0.297 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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