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Record W2060851575 · doi:10.1118/1.1408283

Relationship between phantom failure rates and radiation dose in mammography accreditation

2001· article· en· W2060851575 on OpenAlexaff
Arthur G. Haus, Martin J. Yaffe, Stephen A. Feig, R. Edward Hendrick, P Butler, Pamela Wilcox, Swati Bansal

Bibliographic record

VenueMedical Physics · 2001
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsImaging phantomMammographyDosimeterMedicineNuclear medicineImage qualityDosimetryAutomatic exposure controlQuality assuranceThermoluminescent dosimeterMedical physicsComputer scienceBreast cancerArtificial intelligence

Abstract

fetched live from OpenAlex

The American College of Radiology Mammography Accreditation Program (ACR MAP) reviews both clinical mammograms and a phantom image to assess clinical and technical quality from each mammography unit. The phantom contains details representing fibers (speculations), speck groups (microcalcifications), and masses. The depiction of these structures by the mammographic system is scored by medical physicists. The phantom image is taken using the facility's exposure technique for a 4.2-cm thick breast of average composition. The mean glandular dose (MGD) is determined from a set of thermoluminescent dosimeters placed on top of the chest wall edge of the phantom. Phantom scores and MGD data collected from 1993 to 1999 based on 31 535 unit evaluations are presented in this paper. The relationship between the failure rate for phantom image quality and MGD has been analyzed. While over all doses the phantom failure rate was 11%, for doses of 0.26 to 0.50 mGy the failure rate was 43%. The phantom failure rate fell continuously to about 6% for MGDs in the range of 1.51-2.0 mGy. With further increases in dose, failure rates began to rise. Factors that may account for these results are presented and 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.028
GPT teacher head0.319
Teacher spread0.291 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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