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Adverse Events with Universal Use of Iodixanol for CT

2007· article· en· W2063571950 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsToronto General HospitalMount Sinai HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsIodixanolIohexolMedicineAdverse effectIncidence (geometry)Contrast mediumInternal medicineGastroenterologyRadiologyRenal function

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the incidence of adverse events with the universal use of iodixanol for computed tomography (CT) and compare it with periods when iohexol was used exclusively. METHODS: Iodixanol was used for CT in 15,142 consecutive patients and compared with 22,044 patients who received iohexol. RESULTS: Adverse events were observed in 116 patients (0.77%) who received iodixanol and in 54 patients (0.25%) who received iohexol (P < 0.001). Immediate and delayed adverse events were seen in 76 and 40 patients (0.50% and 0.26%, respectively) who received iodixanol and in 52 and 2 patients (0.24% and 0.01%, respectively) who received iohexol, respectively (immediate, P = 0.002; delayed, P < 0.001). Adverse events with iodixanol and iohexol were as follows: mild, 89% and 98%; moderate, 10% and 2%; and severe, 1% and 0%, respectively. CONCLUSIONS: Adverse events occurred in less than 1% of patients receiving either contrast agent. However, the incidence of immediate and delayed adverse events was significantly higher with iodixanol than iohexol.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.260
Teacher spread0.244 · 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