418 SYSTEMATIC REVIEW OF LEARNING CURVES IN MINIMALLY INVASIVE PARAESOPHAGEAL HERNIA REPAIR: IS THERE ENOUGH EVIDENCE FOR MENTORSHIP IN SURGICAL RESIDENCY?
Bibliographic record
Abstract
Abstract Laparoscopic repair remains the approach of choice for the surgical management of symptomatic paraesophageal hernia (PEH), although robotic techniques are becoming increasingly popular. The learning curve for minimally invasive PEH repair can be variable and little is known of its potential impact upon surgical residents’ training. The aim of this review is to appraise current literature on learning curves in PEH repair and its impact on training and mentorship of surgical residents. Methods Literature searches were performed in three databases: MEDLINE (1980-2020), EMBASE and the Cochrane Library. Search results were screened in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data quality was assessed in accordance with the Newcastle-Ottawa Scale for cohort studies. Results A total of 6 studies were identified; 4 laparoscopic and 2 robotic assisted with 2678 patients assessed in total. One study used a cumulative sum (CUSUM) analysis to define learning with the remainder of studies using between 3 and 11 parameters. 2 studies evaluated trainees’ performance in performing laparoscopic PEH repair when mentored by surgeons who had achieved competence on the learning curve. Both studies reported equivalent clinical outcomes for laparoscopic PEH repair performed by trainees versus consultants, although one study suggested 5-year outcomes were inferior in the trainee group despite mentorship. Conclusion Despite being commonly performed, little is known about the learning curves for minimally invasive PEH repair. Furthermore, whether prior laparoscopic experience confers any advantage to performing robotic PEH repair is not known at present. Further data to help evaluate the learning curve for those performing PEH is required in order to enhance training and permit quicker attainment of competency.
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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 | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".