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Record W2024262846 · doi:10.1118/1.3613107

TU-B-224-01: Impact of the National Institute of Standards and Technology (NIST) on Radiation Dosimetry in Medical Physics

2011· article· en· W2024262846 on OpenAlexaboutno aff
Michael G. Mitch, M McEwen, R Tosh

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsnot available
Fundersnot available
KeywordsNISTDosimetryTraceabilityQuality assuranceMedical physicsPrimary standardAccreditationCalibrationComputer scienceEngineeringMedicinePhysicsNuclear medicineExternal quality assessmentMedical educationOperations management

Abstract

fetched live from OpenAlex

The National Institute of Standards and Technology (NIST) is the National Measurement Institute (NMI) for the US. All dosimetric measurements made in American radiotherapy clinics should be traceable to the primary standards maintained by NIST. The accuracy of the NIST standards, and traceability to the Système Internationale (SI), is ensured through the Bureau International des Poids et Mesures (BIPM), the international laboratory that co-ordinates comparisons between NIST and other NMIs around the world (such as the National Research Council Canada). A continuous calibration chain, therefore, links the measurement of dose in the clinic to the internationally agreed-upon definition of the gray, an essential requirement in ensuring equivalence of clinical dose delivery irrespective of location. Within the US, traceability of radiation dose measurements to the SI is ensured through activities of the Radiation Interactions and Dosimetry (RID) Group at NIST, whose primary mission is to develop, maintain, and disseminate the national measurement standards for the dosimetry of x rays, gamma rays, electrons, and other charged particles. In the case of medical dosimetry, relevant standards are disseminated both directly to the customer and through the AAPM Accredited Dosimetry Calibration Laboratory (ADCL) network by means of calibrations and proficiency testing services, provided to maintain measurement-quality assurance and traceability. The evolving measurement needs of industry, medicine and government provide impetus for the improvement of existing standards and the development of new standards. Research activities in support of this part of the RID Groupˈs mission address a variety of topics in fundamental and applied radiation physics. These efforts are driven partly by advancements in instrumentation technology and partly by the ever expanding domain of measurement standards made possible by such advancements. The widespread adoption of conformal beam therapies, for example, has driven the standards community to develop new approaches for standard reference dosimetry of “nonstandard” beams. At NIST, this has spurred a research program in water calorimetry that is looking into ultrasonic time-of-flight approaches to imaging dose in water. Ultimately, this or similar approaches might lead to new ways of imaging complicated dose distributions in tissue as well as give the standards community new tools for reference dosimetry of present and future beam technologies. In this session, attendees will learn how the accuracy of their clinical measurements is assured as a result of comparisons between NIST and other NMIs around the world as well as NIST proficiency tests and AAPM accreditation of the ADCLs. It will be shown how NIST staff members are active within critical AAPM scientific committees so that measurement needs in the clinic can be addressed by the standards laboratory, resulting in the development of new standards and/or methodologies. Learning Objectives: 1. Understand the impact of measurement standards in general, and in particular the work of primary standards laboratories such as NIST, on clinical radiation dosimetry. 2. Understand the calibration chain from primary standards laboratory to radiotherapy clinic. 3. Understand how NIST interacts with various AAPM committees to ensure that the measurement needs of the user community are met.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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