Radiation Exposure From Diagnostic Imaging in Severely Injured Trauma Patients
Bibliographic record
Abstract
BACKGROUND: Trauma patients often require multiple imaging tests, including computed tomography (CT) scans. CT scanning, however, is associated with high-radiation doses. The purpose of this study was to measure the radiation doses trauma patients receive from diagnostic imaging. METHODS: A prospective cohort study was conducted from June 1, 2004 to March 31, 2005 at a Level I trauma center in Toronto, Canada. All trauma patients who arrived directly from the scene of injury and who survived to discharge were included. Three dosimeters were placed on each patient (neck, chest, and groin) before radiologic examination. Dosimeters were removed before discharge. Surface doses in millisieverts (mSv) at the neck, chest, and groin were measured. Total effective dose, thyroid, breast, and red bone marrow organ doses were then calculated. RESULTS: Trauma patients received a mean effective dose of 22.7 mSv. The standard "linear no threshold" (LNT) model used to extrapolate from effects observed at higher dose levels suggests that this would result in approximately 190 additional cancer deaths in a population of 100,000 individuals so exposed. In addition, the thyroid received a mean dose of 58.5 mSv. Therefore, 4.4 additional fatal thyroid cancers would be expected per 100,000 persons. In all, 22% of all patients had a thyroid dose of over 100 mSv (mean, 156.3 mSv), meaning 11.7 additional fatal thyroid cancers per 100,000 persons would result in this subgroup. CONCLUSION: Trauma patients are exposed to significant radiation doses from diagnostic imaging, resulting in a small but measurable excess cancer risk. This small individual risk may become a greater public health issue as more CT examinations are performed. Unnecessary CT scans should be avoided.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".