Laparoscopic right hemicolectomy with complete mesocolic excision provides acceptable perioperative outcomes but is lengthy — analysis of learning curves for a novice minimally invasive surgeon
Bibliographic record
Abstract
BACKGROUND: Associated with reduced trauma, laparoscopic colon surgery is an alternative to open surgery. Furthermore, complete mesocolic excision (CME) has been shown to provide superior nodal yield and offers the prospect of better oncological outcomes. METHODS: All oncologic laparoscopic right colon resections with CME performed by a single surgeon since the beginning of his surgical practice were retrospectively analyzed for operative duration and perioperative outcomes. RESULTS: The study included 81 patients. The average duration of surgery was 220.0 (range 206-233) minutes. The initial durations of about 250 minutes gradually decreased to less than 200 minutes in an inverse linear relationship (y = -0.58x × 248). The major complication rate was 3.6% ± 4.2% and the average nodal yield was 31.3 ± 4.1. CumulativeSum analysis showed acceptable complication rates and oncological results from the beginning of surgeon's laparoscopic career. CONCLUSION: Developing laparoscopic skills can provide acceptable outcomes in advanced right hemicolectomy for a surgeon who primarily trained in open colorectal surgery. Operative duration is nearly triple that reported for conventional laparoscopic right hemicolectomy. The slow operative duration learning curve without a plateau reflects complex anatomy and the need for careful dissection around critical structures. Should one wish to adopt this strategy either based on some available evidence of superiority or with intention to participate in research, one has to change the view of right hemicolectomy being a rather simple case to being a complex, lengthy laparoscopic surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".