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Record W4382049967 · doi:10.1088/1361-6498/ace194

Reassessing lead protective garment assessment using ICRP 103 tissue weighting factors

2023· article· en· W4382049967 on OpenAlexaff
Joan M Brewer, Kevin Hammerstrom, Thorarin A. Bjarnason

Bibliographic record

VenueJournal of Radiological Protection · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInterior Health
Fundersnot available
KeywordsAlgorithmScrollComputer scienceWeightingMathematicsArtificial intelligenceMachine learningPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Lead protective garments worn by medical staff in the presence of x-rays develop defects over time. This work proposes a novel method of assessing the protective efficacy of the garments as defects develop. The proposed method applies updated radiobiology data from ICRP 103. This work applied the as low as reasonably achievable principle to devise a formula through which a maximum allowed defect area in lead protective garments can be calculated. This formula depends on the cross-sectional areas ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>A</mml:mi> </mml:math> ) and ICRP 103 tissue weighting factors ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>w</mml:mi> <mml:mrow> <mml:mtext>t</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) of the most radiosensitive and overlapping organs protected by the garment, the maximum allowed additional effective dose to the garment wearer due to the defects ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> ), and the unattenuated absorbed dose at the surface of the garment ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> </mml:math> ). The maximum allowed defect areas are separated into three regions: above the waist, below the waist , and thyroid . To be conservative, it was assumed that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> </mml:math> = 50 mGy yr −1 , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> = 0.3 mSv yr −1 . Also conservatively, transmission was assumed to be 0%, as employing a non-zero transmission factor would increase the maximum allowed defect area. Maximum allowed defect areas were as follows: 370 mm 2 for above the waist, 37 mm 2 for below the waist, and 279 mm 2 for the thyroid. These values can be compared to commonly published values which are 670 mm 2 for an apron, 15 mm 2 over the gonads, and 11–20 mm 2 for the thyroid. The proposed method for lead protective garment assessment is highly adaptable as values can be adjusted as radiobiology data are updated, and as values such as radiation dose limits vary across jurisdictions. Future works will include collection of data for unattenuated dose to apron ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> </mml:math> ) as it varies across professions, so that garments may be allowed different defect areas if relegated to individuals of specific professions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.380
Teacher spread0.284 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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