Paediatric radiation therapy without anaesthesia – Are the children moving?
Bibliographic record
Abstract
PURPOSE: Children who require radiation therapy (RT) should ideally be treated awake, without anaesthesia, if possible. Audiovisual distraction is a known method to facilitate awake treatment, but its effectiveness at keeping children from moving during treatment is not known. The aim of this study was to evaluate intrafraction movement of children receiving RT while awake. METHODS: In this prospective study, we measured the intrafraction movement of children undergoing treatment with fractionated RT, using pre- and post-RT cone beam CT (CBCT) with image matching on bony anatomy. Study CBCTs were acquired at first fraction, weekly during RT, and at last fraction. The primary endpoint was the magnitude of vector change between the pre- and post-RT scans. Our hypothesis was that 90 % of CBCT acquisitions would have minimal movement, defined as <3 mm for head-and-neck (HN) treatments and <5 mm for non-HN treatments. RESULTS: A total of 65 children were enrolled and had evaluable data across 302 treatments with CBCT acquisitions. Median age was 11 years (range, 2-18; 1st and 3rd quartiles 7 and 14 years, respectively). Minimal movement was observed in 99.4 % of HN treatments and 97.2 % of non-HN treatments. The study hypothesis of >90 % of evaluations having minimal movement was met. Children who were age >11 years moved less at initial evaluation but tended to move more as a course of radiation progressed, as compared to children who were younger. CONCLUSION: Children receiving RT with audiovisual distraction while awake had small magnitudes of observed intrafraction movement, with minimal movement in >97 % of observed RT fractions. This study validates methods of anaesthesia avoidance using audiovisual distraction for selected children.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".