P-40: Radiation Exposure and Attributable Lifetime Cancer Mortality Risk for Patients with Esophageal Atresia
Bibliographic record
Abstract
Children with esophageal atresia (EA) undergo considerable amounts of diagnostic imaging and consequent radiation exposure throughout their course. We evaluated the radiological procedures performed on EA patients and the cumulative radiation exposure and attributable cancer risk. With IRB approval, patients with EA managed from 2001–2013 were investigated. Demographics, EA subtype and number and type of radiological investigations were gathered. Existing normative data was used to estimate the cumulative radiation exposure and lifetime cancer risk per patient. There were 72 children, 57 with type C atresia. Median follow-up was 5.7 years (mean = 6.33+/−3.9). Table 1 demonstrates the amount of imaging and radiation dose during admission and through follow-up, with an overall median 6.47 mSv/patient. This represents 3 times the annual total normative radiation dose. Radiation exposure in the neonatal period was 5.7 mSV, correlating with an estimated cumulative lifetime mortality risk of cancer of 1:200 per infant. Children with EA are exposed to significant amounts of radiation during hospitalization and throughout follow-up, which may contribute to the documented increase in esophageal cancer rates in these patients. Elimination of superfluous imaging appears warranted, as does direct dosimetry measurements for at-risk patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".