Benchmarks in colorectal surgery: multinational study to define quality thresholds in high and low anterior resection
Bibliographic record
Abstract
BACKGROUND: Benchmark comparisons in surgery allow identification of gaps in the quality of care provided. The aim of this study was to determine quality thresholds for high (HAR) and low (LAR) anterior resections in colorectal cancer surgery by applying the concept of benchmarking. METHODS: This 5-year multinational retrospective study included patients who underwent anterior resection for cancer in 19 high-volume centres on five continents. Benchmarks were defined for 11 relevant postoperative variables at discharge, 3 months, and 6 months (for LAR). Benchmarks were calculated for two separate cohorts: patients without (ideal) and those with (non-ideal) outcome-relevant co-morbidities. Benchmark cut-offs were defined as the 75th percentile of each centre's median value. RESULTS: A total of 3903 patients who underwent HAR and 3726 who had LAR for cancer were analysed. After 3 months' follow-up, the mortality benchmark in HAR for ideal and non-ideal patients was 0.0 versus 3.0 per cent, and in LAR it was 0.0 versus 2.2 per cent. Benchmark results for anastomotic leakage were 5.0 versus 6.9 per cent for HAR, and 13.6 versus 11.8 per cent for LAR. The overall morbidity benchmark in HAR was a Comprehensive Complication Index (CCI®) score of 8.6 versus 14.7, and that for LAR was CCI® score 11.9 versus 18.3. CONCLUSION: Regular comparison of individual-surgeon or -unit outcome data against benchmark thresholds may identify gaps in care quality that can improve patient outcome.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".