Cumulative Effective Doses From Radiologic Procedures for Pediatric Oncology Patients
Bibliographic record
Abstract
OBJECTIVE: Our aim was to estimate the cumulative effective doses (CEDs) from radiologic procedures for a cohort of pediatric oncology patients. METHODS: A retrospective cohort study of the imaging histories of 150 pediatric oncology patients (30 each in 5 subgroups, that is, leukemia, lymphomas, brain tumors, neuroblastomas, and assorted solid tumors) for 5 years after diagnosis was performed. All procedures involving ionizing radiation were recorded, including radiography, computed tomography (CT), nuclear medicine (NM) studies, fluoroscopy, and interventional procedures. CED estimates were calculated. RESULTS: Individual CED estimates ranged from <1 mSv to 642 mSv, with a median of 61 mSv. CT and NM were the greatest contributors; CT constituted 30% of procedures but 52% of the total CED, and NM constituted 20% and 46%, respectively. There was considerable variability between tumor subgroups. CED estimates were highest in the neuroblastoma (median: 213 mSv [range: 36-489 mSv]) and lymphoma (median: 191 mSv [range: 10-642 mSv]) groups and lowest in the leukemia group (median: 5 mSv [range: 0.2-57 mSv]). CONCLUSIONS: CEDs from diagnostic and interventional imaging for pediatric oncology patients vary considerably according to diagnoses, individual clinical courses, and imaging modalities used. Increased awareness may promote strategies to reduce the radiation burden to this population.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".