Radiation Exposure and Attributable Cancer Risk in Patients With Esophageal Atresia
Bibliographic record
Abstract
OBJECTIVES: Cases of esophageal carcinoma have been documented in survivors of esophageal atresia (EA). Children with EA undergo considerable amounts of diagnostic imaging and consequent radiation exposure potentially increasing their lifetime cancer mortality risk. This study evaluates the radiological procedures performed on patients with EA and estimates their cumulative radiation exposure and attributable lifetime cancer mortality risk. METHODS: Medical records of patients with EA managed at a tertiary care center were reviewed for demographics, EA subtype, and number and type of radiological investigations. Existing normative data were used to estimate the cumulative radiation exposure and lifetime cancer risk per patient. RESULTS: The present study included 53 patients with a mean follow-up of 5.7 years. The overall median and maximum estimated effective radiation dose in the neonatal period was 5521.4 μSv/patient and 66638.6 μSv/patient, respectively. This correlates to a median and maximum estimated cumulative lifetime cancer mortality risk of 1:1530 and 1:130, respectively. Hence, radiation exposure in the neonatal period increased the cumulative cancer mortality risk a median of 130-fold and a maximum of 1575-fold in EA survivors. CONCLUSIONS: Children with EA are exposed to significant amounts of radiation and an increased estimated cumulative cancer mortality risk. Efforts should be made to eliminate superfluous imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".