411 LEARNING CURVES IN MINIMALLY INVASIVE ESOPHAGECTOMY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Minimally invasive techniques are being increasingly used in the treatment of esophageal cancer. The learning curve for minimally invasive esophagectomy (MIE) is variable and can have an impact upon training delivered within residency and fellowship programmes. The aims of this review are to critically appraise current literature on the learning curve for MIE, identify what parameter(s) is used to quantify achieving competence and determine if there is evidence of resultant impact on surgical training. Methods A search of the major reference databases (MEDLINE, EMBASE, Cochrane) was performed with no time limits up to the date of the search (February 2020). Results were screened in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and study quality assessed using the Newcastle-Ottawa Scale for cohort studies. Results Twenty-one studies comprising 2720 patients were included- 17 studies reported on a combination of thoracoscopic, hybrid and total MIE, 3 studies reported robotic assisted alone and 1 study evaluated robotic assisted and thoracoscopic esophagectomy. 3 studies used a cumulative sum (CUSUM) analysis to define learning, 1 study used CUSUM and another parameter and 17 studies used one or more parameters. Quantification of surgical competence was variable and ranged from 12–80 cases for robotic surgery and 12–60 cases for other modes of MIE. One study reported trainees achieving MIE skills quicker if mentoring surgeons had attained proficiency on the learning curve. Conclusion Learning curves in MIE remain ill-defined with limited evidence on impact upon training received by residents and fellows. Additionally, the parameters used to define achievement of surgical competency is heterogenous. As minimally invasive techniques are increasingly adopted, specific standards to help define competence need to be identified and agreed on. This could help in designing training programmes and improve the rate of achieving 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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".