Radiation dose assessment of pediatric computed tomography of the chest: the need to consider patient size
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the radiation dose of chest computed tomography (CT) examinations of pediatric patients and the extent to which volume CT dose index (CTDIvol) underestimates radiation dose in comparison to size specific dose estimates (SSDE). METHODS: Single-center, retrospective study of consecutive unenhanced pediatric (age <18 years) chest CTs between October 2015 and October 2016. Radiation dose as well as demographic and clinical data were recorded from 133 chest CTs. Patients were grouped into 4 categories based on mean effective diameter of the chest. SSDE was generated for each patient according to the water equivalent and effective diameter and compared to CTDIvol. Factors associated with higher radiation doses were assessed. RESULTS: CTDIvol underestimated radiation dose by 54.7%, 47.6%, 40.2%, and 31.2% (P < .001) for effective diameter groups 1 to 4, respectively, when compared to SSDE (calculated according to the water equivalent). When calculated according to the effective diameter, CTDIvol underestimated radiation dose by 47.6%, 39.4%, 27%, and 12.3% (P < .001) for effective diameter groups 1 to 4, respectively, when compared to SSDE. CT dose parameters, age, weight, Dw, and mean effective diameter were variables associated with higher radiation doses. CONCLUSION: CTDIvol systematically underestimated radiation dose in comparison to SSDE in pediatric patients submitted to chest CT and should not be used as the primary parameter to monitor CT protocols in these patients. SSDE calculated according to effective diameter also underestimates the radiation dose compared to SSDE calculated based on water equivalent.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.000 | 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 it