Radiation dose from initial trauma assessment and resuscitation: review of the literature.
Bibliographic record
Abstract
BACKGROUND: Trauma care benefits from the use of imaging technologies. Trauma patients and trauma team members are exposed to radiation during the continuum of care. Knowledge of exposure amounts and effects are important for trauma team members. METHODS: We performed a review of the published literature; keywords included "trauma," "patients," "trauma team members," "wounds," "injuries," "radiation," "exposure," "dose" and "computed tomography" (CT). We also reviewed the Board on Radiation Effects Research (BEIR VII) report, published in 2005 and 2006. RESULTS: We found no randomized controlled trials or studies. Relevant studies demonstrated that CT accounts for the single largest radiation exposure in trauma patients. Exposure to 100 mSv could result in a solid organ cancer or leukemia in 1 of 100 people. Trauma team members do not exceed the acceptable occupation radiation exposure determined by the National Council of Radiation Protection and Management. Modern imaging technologies such as 16- and 64-slice CT scanners may decrease radiation exposure. CONCLUSION: Multiple injured trauma patients receive a substantial dose of radiation. Radiation exposure is cumulative. The low individual risk of cancer becomes a greater public health issue when multiplied by a large number of examinations. Though CT scans are an invaluable resource and are becoming more easily accessible, they should not replace careful clinical examination and should be used only in appropriate patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".