Global Benchmarks for Minimal-Invasive Right Hemicolectomy in Adenocarcinoma
Bibliographic record
Abstract
Abstract Background Oncologic right hemicolectomy (rHC) remains the only curative treatment for right-sided colon cancer. Despite its increasing complexity, this procedure is not centralized in many countries, underscoring the need for rigorous assessment and continuous improvement in surgical quality. Benchmarking is a validated quality improvement tool. By defining best achievable outcomes as reference (i.e. benchmarks), it enables centers to evaluate their performance and identify weaknesses or areas for improvement. Aims This analysis aimed to establish benchmarks for outcome parameters in minimal-invasive rHC. Methods We analyzed data from consecutive patients with adenocarcinoma of the colon who underwent minimal-invasive rHC between July 2017 and June 2022 at 19 expert centers across five continents. Ideal cases were defined as elective surgeries for cT1-T3 tumors without distant metastases, major comorbidities, or significant prior abdominal surgeries. Benchmarks were derived for 19 clinically relevant surgical outcomes, including perioperative and oncological parameters, procedure-specific complications, overall morbidity, and mortality. Benchmarks were set at the 75th percentile for negative outcomes and the 25th percentile for positive outcomes across all centers’ medians. Results Among 3154 patients, 686 (22%) qualified as ideal. The proportion of ideal cases varied widely across centers (range: 2 – 51%). Key benchmarks at 3 months were overall morbidity ≤38%, major (Clavien-Dindo ≥3a) complications ≤8%, and 0% mortality. Procedure-specific benchmarks were anastomotic leak ≤3%, and deep surgical site infections ≤6%. Finally, oncologic benchmarks included R0 resection rates 100% and ≥12 lymph nodes harvested ≥96.9%. Ideal compared to non-ideal patients and centers performing ≥500 cases compared to <500 cases annually demonstrated superior outcomes. Conclusion This study demonstrates that, despite its complexity, minimally invasive rHC can be performed with low morbidity and high oncological accuracy. The established benchmarks provide a reference for centers striving to achieve excellence in this procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".