Cumulative summation graphs are a useful tool for monitoring positive surgical margin rates in robot‐assisted radical prostatectomy
Bibliographic record
Abstract
Study Type – Therapy (case series) Level of Evidence 4 What’s known on the subject? and What does the study add? Whilst the technique of robot‐assisted radical prostatectomy has rapidly been adopted by surgeons there is little information on techniques used for quality control when a surgeon is learning this new surgical technique. Cummulative summation graphs have been used in cardiothoracic surgery to monitor complications with good effect and have been shown to be useful in monitoring outcomes from cystectomy. We demonstrate that using the technique of cumulative summation graphs a surgeon can monitor their progress prospectively through a learning curve without having to wait to perform a retrospective analysis. This study demonstrates that margin positive rates in radical prostatectomy can be monitored in real time and adjustments in technique applied to allow a surgeon to continually monitor and improve their surgical results. OBJECTIVE • To explore the usefulness of cumulative summation (CUSUM) graphs for monitoring positive surgical margin (PSM) rates during a surgeon’s transition from open to robot‐assisted radical prostatectomy (RARP). PATIENTS AND METHODS • Data were prospectively collected from patients undergoing RARP by a single surgeon. • Preoperatively all patients were either low or moderate risk under the D’Amico classification system. • A CUSUM graph was charted retrospectively to analyse the PSM rate in patients undergoing RARP for pathological stage T2 (pT2) disease. • Acceptable and unacceptable PSM rates were set at 10% and 15% respectively. RESULTS • From a cohort of 226 patients, 158 patients with pT2 disease were selected. The mean (range) age of these patients was 59.2 (39–73) years, the median (range) Gleason score was 6 (4–9), the mean (range) PSA was 6.43 (0.52–17.5) ng/mL and the mean (range) prostate volume was 44 (18–120) cm 3 . In all, 21 patients had PSMs (13%). • CUSUM graphs were produced and clearly demonstrated the change in PSM rate over time. CONCLUSION • CUSUM graphs are a novel and useful visual representation of the learning curve for surgeons. • PSM rates in patients with pT2 disease are a good outcome to monitor using CUSUM graphs as they are binary and lack the confounding factors associated with other outcomes such as continence and erectile dysfunction. • We advocate the use of CUSUM graphs as a method of quality assurance with the introduction of a robotics programme.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".