Body composition determinants of radiation dose during abdominopelvic CT
Bibliographic record
Abstract
OBJECTIVES: We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. METHODS: Following institutional review board approval, quantitative analysis was performed prospectively on 239 consecutive patients who underwent abdominopelvic CT. DLP, BMI, volumes of abdominal adipose tissue, muscle, bone and solid organs were recorded. RESULTS: = 0.77) revealed that total adipose volume was the strongest predictor of radiation exposure [B (95% CI) = 0.027(0.024-0.030), t=23.068, p < 0.001]. Stepwise linear regression using DLP as the dependent and BMI and total adipose tissue as independent variables demonstrated that total adipose tissue is more predictive of DLP than BMI [B (95% CI) = 16.045 (11.337-20.752), t=6.681, p < 0.001]. CONCLUSIONS: The volume of adipose tissue was the strongest predictor of radiation exposure in our cohort. MAIN MESSAGE: • Individual body composition variables correlate with DLP at abdominopelvic CT. • Total abdominal adipose tissue is the strongest predictor of radiation exposure. • Muscle volume is also a significant but weaker predictor of DLP.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".