Software for the estimation of foetal radiation dose to patients and staff in diagnostic radiology
Bibliographic record
Abstract
Occasionally, it is clinically necessary to perform a radiological examination(s) on a woman who is known to be pregnant or an examination is performed on a woman who subsequently discovers that she was pregnant at the time. In radiological examinations, especially of the lower abdomen and pelvis area, the foetus is directly irradiated. It is therefore important to be able to determine the absorbed dose to the foetus in diagnostic radiology for pregnant patients as well as the foetal dose from occupational exposure of the pregnant worker. The determination of the absorbed dose to the unborn child in diagnostic radiology is of interest as a basis for risk estimates from medical exposure of the pregnant patient and occupational exposure of the pregnant worker. In this paper we describe a simple computer program, FetDose, which calculates the dose to the foetus from both medical and occupational exposures of the pregnant woman. It also calculates the risks of in utero exposure, compares calculated doses with published data in the literature and provides information on the natural spontaneous risks. The program will be a useful tool for the medical and paramedical personnel who are involved with foetal dose (and hence risks) calculations and counselling of pregnant women who may be concerned about in utero exposure of their foetuses.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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".