Surgical education interventions in liver surgery: a systematic review
Bibliographic record
Abstract
The objective of the study was to identify and to evaluate the impact of educational interventions to learn and train liver surgery outside the operating room. A systematic literature search was conducted using PubMed, Web of Science, Embase, and ERIC databases from inception to September 2019 according to the PRISMA guidelines. Studies describing and assessing outcomes of educational interventions in liver surgery, outside the operating room, were included. Neither language nor date of publication restriction was applied. Methodological quality was appraised using NOS-E (Newcastle-Ottawa Scale for Education), and the level of evidence was evaluated based on GRADE (Grades of Recommendation Assessment, Development, and Evaluation) standards. Of the 10,403 screened abstracts, 53 articles were eligible for inclusion, comprising 27 descriptive studies (50.9%), 14 case series assessing any relevant outcome (26.4%), 8 non-randomized controlled trials (15.1%), and 4 randomized controlled studies (7.5%). Almost half (26/53) of the studies did not include any participants, while the remainder of the publications (27/53) involved 1306 learners. The majority of the studies focused on cognitive knowledge (31/53) and/or psychomotor skills training (24/53). Only one publication assessed affective skills. The GRADE score was very low or low in most articles (46/53). Five studies were scored high (5-6) according to NOS-E. Two studies reported data regarding the reliability and validity of employed assessment tools. High-quality studies, particularly well-designed randomized controlled trials that evaluate the effectiveness of simulation-based training on learner behavior and patient outcomes in liver surgery, are still lacking. Forthcoming studies should use robust assessment tools supported by validity evidence.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".