A retrospective audit of cumulative ionising radiation levels in hospitalised pregnant patients
Bibliographic record
Abstract
BACKGROUND: Among hospitalised patients, diagnostic radiation is possibly used least on pregnant patients due to the fear of ionising radiation on the fetus; however, what levels are currently being prescribed. AIMS: To assess the cumulative levels of ionising radiation received by pregnant patients during a single admission to a tertiary hospital. MATERIALS AND METHODS: A retrospective audit of pregnant patients admitted to Flinders Medical Centre, South Australia, Australia, between 2013 and 2017 inclusive was performed. All procedures utilising ionising radiation were collected including conventional radiology, computed tomography, fluoroscopy and nuclear medicine. Individual and cumulative effective doses for mother and fetus were calculated using patient dose reports and published conversion factors. RESULTS: From 547 patients, the median cumulative effective dose was 0.02 mSv and only five patients received more than 10 mSv, with 19.07 mSv the highest dose received. The median fetal cumulative effective dose was 0.01 mSv but only three fetuses received more than 10 mSv, likely due to fetal exclusion in some procedural fields of view. Stays longer than ten days were associated with significantly higher cumulative effective dose, as did those with maternal cardiovascular related admission, for both maternal and fetal exposures. CONCLUSION: These results suggest that pregnant patients are exposed to low doses of ionising radiation, in both individual procedures and cumulative doses. The detrimental risks associated with these levels of ionising radiation are not overt and so clinicians should question which risk is higher, the ionising radiation from the radiological procedures received or the lack of diagnostic information if avoided?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".