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.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".