Use of cumulative summation (CUSUM) as a tool for early feedback and monitoring in robot-assisted radical prostatectomy outcomes and performance
Bibliographic record
Abstract
INTRODUCTION: Today's surgical practice has evolved, with increasing emphasis on quality assurance. Many forms of quality-control monitoring have been suggested, but they are often impractical or difficult to implement. Cumulative summation (CUSUM) is a simple method to provide visual feedback before significant quality issues arise. We present our initial use and practical application of CUSUM in a surgical practice. METHODS: A retrospective analysis was applied to a prospectively collected database of 577 sequential patients who have undergone robot-assisted radical prostatectomy from a single surgeon over a 10-year period. Outcome measures were analyzed with CUSUM, which included a composite complication score, continence rates, length of hospital stay, biochemical recurrence, and need for adjuvant radiation. If any outcomes were out of control, they would cross the CUSUM failure line. RESULTS: CUSUM chart-plotting for incontinence demonstrated an initial upward slope followed by trending to a new safety limit. Additionally, outcomes in complications and biochemical recurrence did not reach the established safety boundaries. Length of stay and radiation outcomes did initially cross the safety line, but were improved over time. CONCLUSIONS: The use of CUSUM in clinical practice can fulfill the need for quality assurance. CUSUM plotting in our practice reflected the initial learning curve, followed by ongoing maintenance and improvement in performance. These changes were consistent with the implementation of changes in surgical techniques. Although this tool was used retrospectively, this strengthens our argument to implement the tool prospectively and assess real-time refinement of surgeon skill. We have demonstrated that CUSUM can be appropriately used to assure quality control in a surgical practice.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".