Intra-operative process efficiency for in-room MRI-guided combined intracavitary/interstitial brachytherapy for cervical cancer
Bibliographic record
Abstract
PURPOSE: Magnetic resonance image-guided brachytherapy (MRgBT) is the gold-standard treatment for cervical cancer. This study examined workflow times in an integrated MRgBT suite and conventional operating room (OR), and factors contributing to intraoperative efficiency. METHODS AND MATERIALS: Consecutive patients with FIGO stage IB-IVA cervical cancer who underwent MRgBT procedures between 2019-2022 were retrospectively reviewed. Workflow times were collected: applicator insertion, MR-imaging, contouring, treatment planning, treatment execution and total procedure time. Procedure durations between applicators and over time were compared. RESULTS: The 161 patients included in this study underwent 267 procedures in the MRgBT suite, and 56 procedures in the OR using ovoid and tandem applicator (O&T, 46%), ring and tandem (R&T, 28%), or Syed-Neblett template (Template, 27%). The median duration (minutes) of each step was: general anesthesia induction (18), applicator insertion (31), MR-imaging (28), parallel contouring (48) and applicator/needle registration & treatment plan optimization (83), and treatment execution (19). Total procedure time was much longer in the OR (488 minutes) than MRgBT suite (205 minutes). Template cases were significantly longer in insertion, MR-imaging, contouring, planning and total procedure time (by 52 minutes) compared with those using the R&T/O&T applicators (p<0.001). Total procedure time for Template cases reduced by 10 minutes/year since 2019 (p<0.001). Regardless of applicator type, total procedure time for subsequent insertions was 21 minutes less than the first (p<0.001). CONCLUSIONS: MRgBT procedure time was longer for Syed-Neblett template cases, but shorter in subsequent insertions. The overall procedure time was much shorter in the integrated MRgBT suite than conventional OR.
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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 | 0.000 | 0.001 |
| 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".