Defining a learning curve for laparoscopic colorectal resections
Bibliographic record
Abstract
PURPOSE: The purpose of this review was to define the learning curve for laparoscopic colorectal resections. METHODS: A prospectively accumulated, computerized database of all laparoscopic colorectal resections performed by three surgeons between April 1991 and March 1999 was reviewed. RESULTS: A total of 461 consecutive resections were evenly distributed among three surgeons (141, 155, and 165). Median operating time was 180 minutes for Cases 1 to 30 in each surgeon's experience and declined to a steady state (150-167.5 minutes) for Cases 31 and higher. Subsequently, Cases 1 to 30 were considered "early experience," whereas Cases 31 and higher were combined as "late experience" for statistical analysis. There were no significant differences between patients undergoing resections in the early experience and those undergoing resections in the late experience with respect to age, weight, or proportion of patients with malignancy, diverticulitis, or inflammatory bowel disease. There were greater proportions of males (42 vs. 54 percent, P = 0.046) and rectal resections performed (14 vs. 32 percent, P = 0.002) in the late experience. Trends toward declining rates of intraoperative complications (9 vs. 7 percent, P = 0.70) and conversion to open surgery (13.5 vs. 9.7 percent, P = 0.39) were observed with experience. Median operating time (180 vs. 160 minutes, P < 0.001) and overall length of postoperative hospital stay (6.5 vs. 5 days, P < 0.001) declined significantly with experience. There was no difference in the rate of postoperative complications between early and late experience (30 vs. 32 percent, P = 0.827). CONCLUSIONS: The learning curve for performing colorectal resections was approximately 30 procedures in this study, based on a decline in operating time, intraoperative complications, and conversion rate. Learning was also extended to clinical care because it was appreciated that patients could be discharged to their homes more quickly.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".