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Record W2144229748 · doi:10.2214/ajr.175.1.1750109

Helical CT Protocols for the Abdomen and Pelvis

2000· article· en· W2144229748 on OpenAlexaff
Martin O’Malley, Elkan F. Halpern, Peter R. Müeller, G. Scott Gazelle

Bibliographic record

VenueAmerican Journal of Roentgenology · 2000
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicinePelvisAbdomenRadiologyMagnetic resonance imagingNuclear medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We surveyed members of the Society of Computed Body Tomography/Magnetic Resonance to evaluate current techniques used for helical CT in the abdomen and pelvis. MATERIALS AND METHODS: The survey was distributed to 70 members (36 institutions) of the Society of Computed Body Tomography/Magnetic Resonance. The survey included general questions related to abdominal and pelvic helical CT and also asked the members to write a protocol for 12 hypothetical requisitions. RESULTS: Thirty-two members (46%) responded, representing 28 institutions (78%). The number of protocols for helical CT of the abdomen and pelvis at each institution ranges from 2 to 35 (median, 11). IV contrast material is administered for 90% (median) of abdominal and pelvic CT examinations. Nonionic contrast material is used for 68% (median) of these examinations. IV contrast material is used by 100% of institutions for tumor staging protocols except for one institution that does not use IV contrast material for lymphoma staging. Fifty percent of the institutions obtain two- or three-phases of liver images for breast cancer staging. For all protocols, the average collimation and reconstruction interval is 7 mm except for renal (5 mm) and adrenal (4 mm) protocols. Rectal contrast material is administered most commonly for colon cancer staging (39% of institutions). CONCLUSION: There is a wide range in the number of protocols used for helical CT in the abdomen and pelvis among the responding institutions. Most protocols include use of nonionic IV contrast material injected at a rate of 3 ml/sec and a collimation of 7 mm.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.020
GPT teacher head0.348
Teacher spread0.328 · 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 designNot applicable
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

Citations43
Published2000
Admission routes1
Has abstractyes

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