SP2.3 - Environmental and non-technical factors influence surgical trainees involvement in laparoscopic appendectomy in Northern Italy: a propensity score matching and survey based multicenter analysis
Bibliographic record
Abstract
Abstract Aims Laparoscopic appendectomy (LA) is an index procedure for surgical residents (R). The trends of R involvement in LA can be a quality benchmark for residency programs. The impact of clinical and non-clinical factors on such trends is still undisclosed. We aimed to investigate the prevalence of LA performed by R in the largest Italian educational network and explore the decision-making behind R involvement in LA. Methods We extracted data from the RESIDENT-1 (R1) multicenter trial registry and performed a propensity score matching (PSM) analysis to identify clinical and environmental differences according to the operator, surgeons(S) vs R. Additionally, to explore environmental and non-technical factors influencing the choice of the primary operator, we administered a survey to S and R in the R1 network. Results We enrolled 653 LA from October 2019 to October 2022, 234 (35.89%) were performed by R. We compared 231 per group after PSM. In academic hospitals and dedicated emergency surgery units LA were more frequently approached by R (57.14%vs26.84%, 51.08%vs19.05% p<0.001, respectively). The survey showed discrepant perspectives, R prioritize clinical factors, as the presence of complicated disease (25.53%vs.8.33%, p<0.015), whilst non-technical and environmental factors such as year of residency (63.1%vs.44.7%,p=0.05) and punctuality (47.2%vs.23.4%,p=0.041) were more important for S. Both groups agreed that a perioperative feedback system would improve the process of R involvement in LA. Conclusion Our study report a low rate of LA performed by R in northern Italy, primarily influenced by non-clinical factors. Furthermore, dedicated pathways and a well-structured perioperative teaching method could optimize the teaching process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".