QuickStart radiotherapy: an inter-professional approach to expedite radiotherapy treatment in early breast cancer
Bibliographic record
Abstract
Abstract Background and purpose This study aims to develop an expedited radiotherapy (RT) process and evaluate its time savings in women requiring whole breast RT. Material and methods An inter-professional RT team streamlined the computed tomography (CT) simulation and treatment pathway for a ‘QuickStart’ process. Target delineation was performed by an advanced practice radiation therapist and approved by the radiation oncologist (RO) for planning. Automated breast planning software was used for treatment planning and standard quality checks were performed. To assess time savings, the initial 25 QuickStart patients were matched with women who underwent whole breast simulation on the same day (±3 days), treated using the conventional process. Results A total of 73 post-lumpectomy women were treated through the QuickStart process; the median consent-to-RT was 2 days (range: 0–13) and the mean CT simulation-to-RT treatment was 2 hours and 42 minutes (SD 0:30). In the subgroup analysis, QuickStart patients saved an average of 11 days from CT simulation-to-RT and had shorter median wait-times for both surgery/chemotherapy-to-RT (60 versus 38 days; p=0·002) and consultation-to-RT (7 versus 20 days; p<0·001). Conclusions Through inter-professional team efforts and the application of automated planning software, we have achieved a process that significantly decreases patient wait-times while maintaining the quality of whole breast RT.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".