Strategies for calculating contrast media dose for chest CT
Bibliographic record
Abstract
BACKGROUND: Total body weight (TBW) is a frequently used contrast media (CM) strategy for dose calculation in enhanced CT, yet it is suboptimal as it lacks consideration of patient characteristics, such as body fat percentage (BFP) and muscle mass. Alternative CM dosage strategies are suggested by the literature. Our objectives were to analyze the CM dose impact when adjusting to body composition using methods of obtaining lean body mass (LBM) and body surface area (BSA) along with its correlation with demographic factors in contrast enhanced chest CT examinations. METHODS: Eighty-nine adult patients referred for CM thoracic CT were retrospectively included, categorized as either normal, muscular, or overweight. Patient body composition data was used to calculate the CM dose according to LBM or BSA. LBM was calculated with the James method, Boer method, and bioelectric impedance (BIA). BSA was calculated using the Mostellar formula. We then correlated the corresponding CM doses with demographic factors. RESULTS: BIA demonstrated the highest and lowest calculated CM dose in muscular and overweight groups respectively, compared to other strategies. For the normal group, the lowest calculated CM dose was achieved using TBW. The calculated CM dose was more closely correlated with BFP using the BIA method. CONCLUSIONS: The BIA method is more adaptive to variations in patient body habitus especially in muscular and overweight patients and is most closely correlated to patient demographics. This study could support utilizing the BIA method for calculating LBM for a body-tailored CM dose protocol for enhanced chest CT examinations. RELEVANCE STATEMENT: The BIA-based method is adaptive to variations in body habitus especially in muscular and overweight patients and is closely correlated to patient demographics for contrast-enhanced chest CT. KEY POINTS: • Calculations based on BIA showed the largest variation in CM dose. • Lean body weight using BIA demonstrated the strongest correlation to patient demographics. • Lean body weight BIA protocol may be considered for CM dosing in chest CT.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".