Effect of Same-Day Volumetric Modulated Arc Therapy on Resource Utilization in Rapid Access Palliative Radiotherapy Clinics Using a Radiation Oncologist-Initiated Automated Planning Script
Bibliographic record
Abstract
PURPOSE: Rapid access palliative (RAP) radiotherapy (RT) clinics enable patients to access urgent same-day consultation, simulation, and treatment. This study aimed to examine the effect of same-day volumetric modulated arc therapy (VMAT) implementation using the Northern Plan Automation Service Treatment Planning Automation Service (NoPAUSE-TPAS) on patient throughput in RAP-RT clinics at a regional cancer center. Methods:This retrospective study included all patients seen in RAP-RT clinics between February and August 2024 following the introduction of NoPAUSE-TPAS, compared to a data set from January to July 2019, prior to the introduction of NoPAUSE-TPAS. Baseline characteristics were analyzed using descriptive statistics. Measures of resource utilization and quality pertaining to consultations, computed tomography simulation slots, and treatment delivery were assessed. RESULTS: RAP-RT clinics saw 202 patients in 2024 and 213 in 2019. In 2019, 195 (91.5%) patients received RT to 249 sites, compared to 189 (93.6%) patients who received RT to 246 sites in 2024. Most patients (n=148 (69.5%) in 2019; n=143 (70.8%) in 2024) received RT to one site. Bone was the most common site treated (n=176 (70.7%) in 2019; n=177 (71.9%) in 2024). The most common fractionation was 8 Gy/1 (n=128 (51.5%) in 2019; n=156 (63.4%) in 2024). Of the bone metastases, 117 (66.5%) were treated with a single fraction in 2019, compared to 144 (81.4%) in 2024. Most patients (n=185 (94.9%) in 2019; n=171 (90.5%) in 2024) started RT the same date as consultation. Within the 2024 cohort, 133 (54.1%) sites were treated with an unplanned technique and 113 (45.9%) with VMAT. Of the sites treated, 192 (78%) were eligible for NoPAUSE-TPAS. Of the eligible sites, 113 (58.9%) received treatment with VMAT, with 96 (84.9%) of these delivered on the same date as consultation. The median time for NoPAUSE-TPAS optimization was 12 minutes. Conclusions:Same-day VMAT using NoPAUSE-TPAS was implemented in RAP-RT clinics with no scheduling changes impacting patient throughput and similar resource utilization compared to historical data. Utilizing automation technology to improve efficiency can enable same-day VMAT for palliative RT.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".