Poster — Wed Eve—06: Organ Equivalent and Effective Doses from Diagnostic Radiology Procedures
Bibliographic record
Abstract
Diagnostic radiology imaging techniques including conventional radiography, fluoroscopy and computed tomography will continue to provide tremendous benefits to modern healthcare. The quantification of radiation risks associated with radiological examinations has been the subject of interest for some time now with the increased used of x‐rays in the radiology departments. Effective dose which is a risk‐weighted measure of radiation exposure to organs in the body associated with radiological examination(s) is considered a better indicator of radiological risk. We have therefore undertaken a survey to investigate patients' equivalent and effective doses from various radiological examinations. This will provide a meaningful indication of patient exposure, taking into account the details of the radiological technique used for particular examinations and help to establish some reference or guidance dose values for radiological examinations performed at this hospital. It would also allow us to monitor any changes over time that might arise from ageing equipment or changing protocols, as well as giving us a means to compare doses with other hospitals and regions. Organ and effective dose were estimated for 94 patients (60 adults and 34 children) who underwent computed tomography examination of the head, chest and abdomen‐pelvis areas and for 338 patients who had conventional radiography examinations. The OrgDose program was used to calculate for all patients doses to 13 organs with the International Commission on Radiological Protection specified tissue weighting factors, 10 organs listed as remainder organs and some additional organs such as lenses of eyes, heart, head, leg, and trunk regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".