Po‐Thur Eve General‐34: Normalized data for the estimation of fetal radiation dose from radiotherapy of the breast
Bibliographic record
Abstract
There can be several reasons why a pregnant patient may receive a radiological examination. It could have been a planned exposure or may have resulted from an emergency when a thorough evaluation of pregnancy was impractical or the pregnancy was unsuspected during the examination. With younger women being diagnosed with breast cancer, the likelihood of the later will increase in the radiotherapy departments. Whatever the reason, when presented with a pregnant patient who has been exposed to ionizing radiation, the dose to the fetus should be assessed. However, a major source of uncertainty in the estimation of fetal dose is the influence of fetal size and position as these changes with gestational age. We have investigated doses to the fetus from radiotherapy of the breast of a pregnant patient using an anthropomorphic phantom. Data for estimating fetal dose that takes into account the size and depth within the maternal abdomen for different treatment techniques have been provided. The data indicate that fetal dose is dependent on both depth and gestational age and hence these factors should always be considered when estimating dose. The data shows that dose can be underestimated up to about 10% or overestimated up to about 30% if the dose to the uterus is assumed instead of the actual fetal dose. It can also be underestimated up to about 23% or overestimated up to about 12% if a mean depth of 9cm is assumed, instead of using the actual depth of the fetus within the maternal abdomen.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".