Dosimetric Feasibility of Cone-Beam CT in Pediatric Image-Guided Retrograde Gastrostomy Tube Insertions
Bibliographic record
Abstract
PURPOSE: Cone-beam computed tomography (CBCT) in interventional radiology allows volumetric imaging with open patient access. This work aimed to assess radiation dose metrics of CBCT in simulated image-guided retrograde gastrostomy (IGRG) tube insertions in pediatric anthropomorphic phantoms and to compare them to measured radiation dose metrics obtained using fluoroscopy during clinical IGRG tube insertions in children. METHODS: ). These 3 stages in 30 patients (3 × 30 = 90 patient stages) were compared to dose indices measured from 4 CBCT acquisition protocols acquired in pediatric phantoms. RESULTS: The mean proportion of radiation dose during planning, insertion, and confirmation was 35%, 38% and 27%, with mean reference-point air kerma (range) measured to be 1.0 (0.02-6.0) mGy, 0.9 (0.03-4.1) mGy, and 0.7 (0.04-3.7) mGy, respectively. Cone-beam computed tomography dose varied greatly depending on technical parameters and protocol selection, ranging from 0.7 to 39.3 mGy. In 19% of patient stages, the most dose-sparing CBCT protocol evaluated on phantoms delivered less radiation than the radiation dose indices recorded from patient's fluoroscopy. CONCLUSIONS: From a dosimetric perspective, radiation delivered in CBCT can vary widely, yet can be appreciably low. With appropriate CBCT protocol selection, the radiation dose delivered may be sufficiently low to warrant consideration for use, if clinically needed during difficult IGRG tube insertions, and satisfy the interventionalist's benefit-risk assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".