2196 OBESITY TRIPLES THE RADIATION EXPOSURE OF STONE PROTOCOL CT
Bibliographic record
Abstract
You have accessJournal of UrologyImaging/Radiology: Uroradiology II1 Apr 20122196 OBESITY TRIPLES THE RADIATION EXPOSURE OF STONE PROTOCOL CT Zachariah G. Goldsmith, Agnes Wang, Chu Wang, Giao Nguyen, Carolyn Lowry, Gastón M. Astroza, Muhammad W. Iqbal, Michael N. Ferrandino, Leo Ding, Matthew D. Belley, Greta Toncheva, Terry T. Yoshizumi, Glenn M. Preminger, and Michael E. Lipkin Zachariah G. GoldsmithZachariah G. Goldsmith Durham, NC More articles by this author , Agnes WangAgnes Wang Durham, NC More articles by this author , Chu WangChu Wang Durham, NC More articles by this author , Giao NguyenGiao Nguyen Durham, NC More articles by this author , Carolyn LowryCarolyn Lowry Durham, NC More articles by this author , Gastón M. AstrozaGastón M. Astroza Durham, NC More articles by this author , Muhammad W. IqbalMuhammad W. Iqbal Durham, NC More articles by this author , Michael N. FerrandinoMichael N. Ferrandino Durham, NC More articles by this author , Leo DingLeo Ding Durham, NC More articles by this author , Matthew D. BelleyMatthew D. Belley Durham, NC More articles by this author , Greta TonchevaGreta Toncheva Durham, NC More articles by this author , Terry T. YoshizumiTerry T. Yoshizumi Durham, NC More articles by this author , Glenn M. PremingerGlenn M. Preminger Durham, NC More articles by this author , and Michael E. LipkinMichael E. Lipkin Durham, NC More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.2370AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Computed tomography (CT) utilization over the last three decades has exponentially increased. In urology, stone patients constitute a high-risk population for radiation exposure, as CT is frequently employed to diagnose and follow stone formers. Paralleling this growth in CT use is the epidemic of obesity. We theorize that there is a different amount of energy required to penetrate the tissues in an obese compared to a non-obese adult. METHODS A validated, anthropomorphic male phantom (Model 701-D; CIRS, Inc, Norfolk, VA) was scanned on a General Electric Lightspeed 64-slice Volume CT according to our institutional protocol. The obese model consisted of the phantom wrapped in two Custom Fat Layers for Model 701-D, E1397-1 and E1397-2, which have been verified to have the same radiographic tissue density as fat. High sensitivity metal oxide semiconductor field effect transistors (MOSFET) dosimeters (Best Medical, Ottawa, Canada) were placed at 20 organ locations in the phantom and used to measure organ specific radiation doses. The non-obese model has a BMI of 24 kg/m2 while the obese model has a BMI of 30 kg/m2. Three runs of the renal stone protocol CT were performed on each phantom, obese and non-obese. Organ-specific doses were measured and the effective doses were calculated. A paired student's t-test was used for statistical analysis. RESULTS For both models, the bone marrow received the highest dose, with the skin receiving the second highest dose. The mean effective dose for the non-obese model was 3.04 ± 0.34 milli-sieverts (mSv), while the effective dose for the obese model was 9.67 ± 0.44 mSv, P<0.0001. CONCLUSIONS The effective dose of stone protocol CT for obese patients is more than three times the dose for a non-obese patient. To our knowledge, this has not been reported before in the literature. When ordering CTs, the BMI of the patient should also be taken into consideration. The implication of this finding extends beyond the urologic stone population and adds to our understanding of radiation exposure from medical imaging. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e885-e886 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Zachariah G. Goldsmith Durham, NC More articles by this author Agnes Wang Durham, NC More articles by this author Chu Wang Durham, NC More articles by this author Giao Nguyen Durham, NC More articles by this author Carolyn Lowry Durham, NC More articles by this author Gastón M. Astroza Durham, NC More articles by this author Muhammad W. Iqbal Durham, NC More articles by this author Michael N. Ferrandino Durham, NC More articles by this author Leo Ding Durham, NC More articles by this author Matthew D. Belley Durham, NC More articles by this author Greta Toncheva Durham, NC More articles by this author Terry T. Yoshizumi Durham, NC More articles by this author Glenn M. Preminger Durham, NC More articles by this author Michael E. Lipkin Durham, NC More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.321 | 0.098 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".