Prevalence of Risk Factors for Contrast-Induced Nephrotoxicity in Outpatients Undergoing Intravenous Contrast-Enhanced Computed Tomography Studies
Bibliographic record
Abstract
OBJECTIVE: To document the prevalence and pattern of risk factors for contrast-induced nephrotoxicity (CIN) in an outpatient population referred for contrast-enhanced computed tomography (CT) (CECT) studies. METHODS: Over a 4-month period, 3261 patients presenting for CT studies were asked to consent to a survey of CIN risk factors. Among these patients, 957 were outpatients who received contrast material before the CT study. Demographic information and available serum creatinine (SCr) data for these patients were extracted from medical records and then analysed. A telephone questionnaire to document risk factors was administered to 200 of these patients, who were outpatients not referred from the emergency department or on dialysis. RESULTS: Among the 200 surveyed patients, 69% reported risk factors for CIN or for kidney function impairment (low estimated glomerular filtration rate [eGFR]) at baseline. Among these patients, 43% reported multiple (2 or more) risk factors. Patients older than the mean age of 53 years had a higher incidence of having multiple risk factors when compared with those younger than this age. Patients with kidney function impairment at baseline had a higher incidence of having multiple risk factors when compared with those with normal kidney function. Among the patients with no SCr investigation in the 3 months preceding a CECT, 64% had multiple risk factors. In the study population of 957 outpatients undergoing CECT, 52% had SCr measurements within 3 months before the study. An eGFR of less than 60 mL/min/1.73 m(2) was found in 17% of the study population, and, in 0.6%, the eGFR was less than 30 mL/min/1.73 m(2). CONCLUSION: In an ambulatory outpatient population, many patients presenting for outpatient CECT studies will have risk factors for CIN or for kidney function impairment (low eGFR) at baseline. Kidney function assessment, therefore, is indicated in this patient population before CECT.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".