Establishment of local diagnostic reference levels for common adult CT examinations: a multicenter survey in Addis Ababa
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background In medical imaging, a computed tomography (CT) scanner is a major source of ionizing radiation. All medical radiation exposures should be justified and optimized to meet the clinical diagnosis. Thus, to avoid unnecessary radiation doses for patients, diagnostic reference levels (DRLs) have been used. The DRLs are used to identify unusually high radiation doses during CT procedures, which are not appropriate for the clinical diagnosis. It has been successfully implemented in Europe, Canada, Australia, the United States, several industrialized countries, and a few underdeveloped countries. The present study aimed to establish DRLs for the head, chest, and abdominopelvic (AP) CT procedures in Addis Ababa, Ethiopia. Methods A pilot study identified the most frequent CT examinations in the city. At the time of the pilot, eighteen CT scan facilities were identified as having functioning CT scanners. Then, on nine CT facilities (50% of functional CT scanners), a prospective analysis of volume CT dose index (CTDI vol ) and dose length product (DLP) was performed. We collected data for 838 adult patients’ head, chest, and AP CT examinations. SPSS version 25 was used to compute the median values of the DLP and CTDI vol dose indicators. The rounded 75th percentile of CTDI vol and DLP median values were used to define the DRLs. The results are compared to DRL data from the local, regional, and international levels. Result The proposed DRLs using CTDI vol (mGy) are 53, 13, and 16 for the head, chest, and AP examinations respectively, while the DLP (mGy.cm) for the respective examinations were 1210, 635, and 822 mGy.cm. Conclusion Baseline CT DRLs figures for the most frequently performed in Addis Ababa were provided. The discrepancies in dose between CT facilities and as well as between identical scanners suggests a large potential for dose optimization of examinations. This can be actually achieved through appropriate training of CT technologists and continuous dose audits.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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