The Minimally Invasive Esophagectomy (MIE) App: a novel teaching tool for minimally invasive esophagectomy
Bibliographic record
Abstract
Background: Minimally invasive esophagectomy (MIE) is an alternative to open surgery for patients requiring esophageal resection. Despite the potential of MIE to reduce patient morbidity, the complexity of the procedure has made it challenging to learn, therefore limiting widespread adoption. An interactive computer- and smartphone-based application has been developed to facilitate comprehensive teaching of MIE. Methods: The entire Ivor-Lewis MIE procedure was broken down into fundamental steps. A text-based atlas was created to guide the learner through all phases of the operation. Surgical videos of the associated steps were also captured and annotated. Finally, a 3-dimensional (3D) modeling platform was used to create a simulated environment in which learners can test their knowledge of the procedure. Results: Our application delivers an easy-to-use, multi-platform learning tool. It combines didactic text-based learning with high-definition videos and a realistic, interactive simulator. Conclusions: The MIE App allows individuals to learn and simulate each fundamental step of Ivor-Lewis MIE. It provides accompanying videos as well as a detailed text-based atlas. The application is available through multiple platforms (PC/Mac, Android, and iOS) and provides objective evaluation via an interactive competency assessment tool. We hope that this application will serve as an adjunct to traditional operating room exposure, allowing for accelerated adoption of MIE.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".