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Record W2338701808 · doi:10.1093/rpd/ncw102

A Radiographic Diagnostic Reference Level Survey Using Patient and Phantom Data

2016· article· en· W2338701808 on OpenAlexaff
R. Cropp, Petar Seslija, Yogesh Thakur

Bibliographic record

VenueRadiation Protection Dosimetry · 2016
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsImaging phantomMedicineRadiographyPercentileDose area productNuclear medicineDigital radiographyComputed radiographySupine positionPelvisRadiation exposureRadiologyMedical physicsSurgeryMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

A diagnostic reference level (DRL) survey was conducted for seven common radiographic projections across an integrated health region, covering 27 hospitals and clinics. The projections surveyed were Chest Posterior-Anterior (PA) and Lateral, Abdomen Supine and Upright, L-spine Anterior-Posterior (AP) and Lateral, and Pelvis. Dose area product (DAP) values were collected from patient examinations in 43 digital radiography (DR) rooms and in 18 conventional rooms which use computed radiography (CR). In each room, data were collected for between 10 and 20 patient exposures for each surveyed projection. In addition, for each projection and room, a DAP value was measured for the exposure of a uniform acrylic phantom of standardized thickness. DAP values in DR rooms were found to be significantly lower than in CR rooms (p < 0.05). Therefore, DR and CR rooms were analyzed separately. Based on the survey results, separate DRLs are presented for DR and CR rooms, for both patient and phantom DAP values. DRLs have been set at the 75th percentile values; 25th percentile and median values are also presented to characterize the range of observed values. When identifying which rooms were above the DRLs, the patient and phantom data identified a partially different set of rooms. Possible reasons for these differences, including uncertainties in the patient data, are discussed.

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)0.002
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 score0.673
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.165
GPT teacher head0.330
Teacher spread0.165 · 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
Published2016
Admission routes1
Has abstractyes

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