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Record W1974983086 · doi:10.2214/ajr.07.2816

Conventional and Reduced Radiation Dose of 16-MDCT for Detection of Nephrolithiasis and Ureterolithiasis

2007· article· en· W1974983086 on OpenAlexaff
Erik K. Paulson, Carolyn J. Weaver, Lisa M. Ho, Lucie C. Martin, Jianying Li, James Darsie, Donald P. Frush

Bibliographic record

VenueAmerican Journal of Roentgenology · 2007
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsMontfort Hospital
Fundersnot available
KeywordsMedicineRadiation doseRadiologyMedical physicsNuclear medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Our purpose was to prospectively compare the reader compatibility and acceptability of a range of reduced-dose 16-MDCT images with standard-dose 16-MDCT images for the detection of nephroureterolithiasis using a dose reduction simulation technique. SUBJECTS AND METHODS: The study was HIPAA compliant and institutional review board approved. Fifty consecutive patients with suspected nephrolithiasis were recruited to undergo conventional renal stone unenhanced 16-MDCT with at least 160 mA. Noise was then artificially introduced to simulate levels of 70, 100, and 130 mA. Three blinded independent readers interpreted the original and simulated-dose scans for the location and number of renal and ureteral calculi and secondary signs of obstruction using a 5-point confidence scale. RESULTS: Reader acceptability of scans was inversely related to noise. There was no significant reduction in readers' confidence in detection or exclusion of renal collecting system calculi with simulated reduction of mA of 70, 100, and 130 compared with the standard-dose study. However, for ureteral calcifications, there was a decrease in confidence for the detection or exclusion of ureterolithiasis at an mA of 70 (35 mAs). CONCLUSION: An mA as low as 70 (35 mAs) is acceptable for evaluation of nephrolithiasis. However, the evaluation of ureterolithiasis is compromised with an mA of 70.

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)0.000
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 score0.631
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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

Citations52
Published2007
Admission routes1
Has abstractyes

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