Assessment of Cirrhotic Liver Enhancement with Multiphasic Computed Tomography Using a Faster Injection Rate, Late Arterial Phase, and Weight-Based Contrast Dosing
Bibliographic record
Abstract
PURPOSE: This study aimed to update our liver computed tomography (CT) protocol according to published guidelines, and to quantitatively evaluate the effect of these modifications. METHODS: The modified liver CT protocol employed a faster injection rate (5 vs 3 mL/s), later arterial phase (20-second vs 10-second postbolus trigger), and weight-based dosing of iodinated contrast (1.7 mL/kg vs 100 mL fixed dose). Liver and vascular attenuation values were measured on CTs of patients with cirrhosis from January to September 2015 (old protocol, n = 49) and from October to December 2015 (modified protocol, n = 31). CTs were considered adequate if liver enhancement exceeded 50 Hounsfield units (HU) in portal venous phase, or when the unenhanced phase was unavailable, if a minimum iodine concentration of 500 mg I/kg was achieved. Attenuations and iodine concentrations were compared using the t test and the number of suboptimal studies was compared with Fisher's exact test. RESULTS: CTs acquired with the modified protocol demonstrated higher aortic (P = .001) and portal vein (P < .0001) attenuations in the arterial phase as well as greater hepatic attenuation on all postcontrast phases (P = .0006, .002, and .003 for arterial, venous, and equilibrium phases, respectively). Hepatic enhancement in the portal venous phase (61 ± 15 HU vs 51 ± 16 HU; P = .0282) and iodine concentrations (595 ± 88 mg I/kg vs 456 ± 112 mg I/kg; P < .0001) were improved, and the number of suboptimal studies was reduced from 57% to 23% (P = .01). CONCLUSIONS: A liver CT protocol with later arterial phase, faster injection rate, and weight-based dosing of intravenous contrast significantly improves liver enhancement and iodine concentrations in patients with cirrhosis, resulting in significantly fewer suboptimal studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".