Learning Curve in Robotic Colorectal Surgery
Bibliographic record
Abstract
Background and Objectives: Robotic platforms are increasingly employed in colorectal surgery for their technical and ergonomic benefits. However, surgeons face a significant learning curve, and there is no standardized definition or threshold to proficiency. This systematic review aimed to evaluate published evidence on learning curves in robotic colorectal surgery, focusing on proficiency thresholds, analytic methodologies, and the effect of experience on clinical and oncological outcomes. Methods: A systematic literature search of PubMed was performed through April 7, 2025. Studies reporting learning curve data for robotic colorectal procedures were included. Screening and selection were conducted using Rayyan. Extracted data included operative time, case numbers to proficiency, conversion and complication rates, and oncological metrics. Study quality was assessed using the Newcastle-Ottawa Scale. A narrative synthesis was undertaken due to heterogeneity in study design and outcomes. Results: Nineteen studies met inclusion criteria. The number of cases required to reach proficiency ranged from 15-55, with operative time being the most analyzed parameter. CUSUM and RA-CUSUM were the predominant analytic methods. Improved outcomes such as reduced complications, lower conversion rates, and enhanced oncological quality were generally observed in the post-proficiency phase. Variability in learning curve definitions and analytic approaches was significant across studies. Conclusion: Robotic colorectal surgery involves a measurable learning curve that impacts both technical and patient-centered outcomes. While most studies demonstrate improved metrics with experience, the lack of standardized methodology limits cross-study comparisons. Structured training pathways and consensus on learning curve analysis are needed to support safe adoption and credentialing in robotic colorectal surgery.
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 | 0.025 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".