Learning curve for laparoscopic cholecystectomy has not been defined: A systematic review
Bibliographic record
Abstract
BACKGROUND: Laparoscopic cholecystectomy is one of the most performed surgeries worldwide but its learning curve is still unclear. METHODS: A systematic review was conducted according to the 2009 Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Two independent reviewers searched the literature in a systematic manner through online databases, including Medline, Scopus, Embase, and Google Scholar. Human studies investigating the learning curve of laparoscopic cholecystectomy were included. The Newcastle-Ottawa scale for cohort studies and the GRADE scale were used for the quality assessment of the selected articles. RESULTS: Nine cohort studies published between 1991 and 2020 were included. All studies showed a great heterogeneity among the considered variables. Seven articles (77.7%) assessed intraoperative variables only, without considering patient's characteristics, operator's experience, and grade of gallbladder inflammation. Only five articles (55%) provided a precise cut-off value to see proficiency in the learning curve, ranging from 13 to 200 laparoscopic cholecystectomies. CONCLUSIONS: The lack of clear guidelines when evaluating the learning curve in surgery, probably contributed to the divergent data and heterogeneous results among the studies. The development of guidelines for the investigation and reporting of a surgical learning curve would be helpful to obtain more objective and reliable data especially for common operation such as laparoscopic cholecystectomy.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".