Incidence and Risk Factors of Contrast Nephropathy After Tace In Patients with Liver Cancer and Chronic Kidney Disease
Bibliographic record
Abstract
PURPOSE: Incidence of contrast induced nephropathy (CIN) and related risk factors in patients with liver cancer and chronic kidney disease after trans-catheter arterial chemoembolization (TACE) is higher. The purpose of this study was to investigate the feasibility and safety of TACE therapy in such patients. METHODS: A retrospective analysis was performed on 103 patients with liver cancer and chronic kidney disease who underwent TACE treatments. TACE was performed according to Seldinger's technique of arterial embolization with minor modifications. Based on CIN diagnostic criteria, patients were divided into non-CIN (n=89) and CIN (n=14) groups. Multiple clinical parameters were assessed for the two groups after TACE. Serum creatinine levels were measured 48-72 h after TACE. RESULTS: Tumor size (>5 cm), TACE frequency, contrast agent dosage, solitary kidney, volume of iodized oil used in the TACE (ml) and urea levels were significantly higher in CIN group in comparison with the non-CIN group, while serum albumin and haemoglobin levels were significantly lower. Multivariate logistic regression analysis confirmed that the volume of iodized oil and TACE frequency were significantly positively correlated, and serum albumin level was negatively correlated in the CIN group. CONCLUSION: Volume of iodized oil, TACE frequency and low serum albumin levels were found to be independent risk factors for CIN after TACE. Thus, it is safe and feasible for hepatocellular carcinoma patients with chronic kidney disease to receive TACE treatment, but adverse events management after TACE needs to be addressed.
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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 | 0.002 |
| 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".