Hepatic enhancement differences when dosing iodinated contrast media according to total versus lean body weight
Bibliographic record
Abstract
BackgroundRecent studies suggest potentially improved inter-patient variability in hepatic enhancement by dosing contrast media (CM) according to lean body weight (LBW); however, studies vary in dosing strategy and most involved solely Japanese patients. PurposeTo compare the magnitude and inter-patient variability in mean hepatic enhancement (MHE) when dosing CM according to total body weight (TBW) versus LBW in a Western population. Material and MethodsWith ethics approval, this study comprised two parts: (i) 100 CTs acquired with 1.3 mL Isovue 370/kg TBW were analyzed; (ii) 108 patients were consented for LBW dosing at 1.9 mL/kg (max. 150 mL, both groups). Liver attenuations were obtained from regions of interest. The MHE, MHE per gram of iodine (MHE/I), and adjusted MHE (aMHE = MHE/(I/TBW or LBW)) were calculated. We compared patient populations (Fisher’s exact test, t-tests) and inter-patient variability (F-tests of variances in MHE) and performed linear regressions of MHE and aMHE. ResultsCohorts were similar in age, sex, TBW, LBW, and total CM dose. MHE was higher in part 2 (63.1 ± 13 vs. 56.3 ± 12 HU, P = 0.0001) but variances were similar (P > 0.7). In part 2, men received more CM (P = 0.0002) and MHE was higher (P = 0.0002); women received less CM (P = 0.053) but showed a non-significant trend for greater MHE (P = 0.07). MHE/I was higher for women in part 2 (P = 0.01) and stable in men (P = 0.72). Linear regressions showed no lines of best-fit with non-zero slopes, for both sexes and study parts. ConclusionWith CM dose constant, LBW dosing yielded a higher magnitude in MHE but did not reduce inter-patient variability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".