Radiation Exposure at Chest CT: A Statement of the Fleischner Society
Bibliographic record
Abstract
The introduction of helical single-detector row computed tomography (CT) and, more recently, multi-detector row CT has greatly increased the clinical indications for CT. Correspondingly, CT examinations now account for greater than one-half of the radiation dose due to medical procedures in the population of North America. The level of CT radiation dose, especially in the pediatric population, is of concern to radiologists, medical physicists, government regulators, and the media. This review addresses this problem with particular reference to radiation dose in chest CT. Specifically it outlines the topics of measurement units used to quantify radiation exposure, factors affecting CT scanner dose efficiency, scanner settings that determine the administered radiation dose, and radiation dose reduction in chest CT. A table of reference dose values is provided. Given the wide variation documented in chest CT radiation exposure, the authors suggest that reference standards be promoted to minimize excessive CT radiation exposure. In addition, further research into the complex relationship between radiation exposure, image quality, and diagnostic accuracy should be encouraged in order to establish the minimum radiation dose necessary to provide adequate diagnostic information for standard clinical questions.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".