A Novel Low-Cost Simulator for Laparoscopic Inguinal Hernia Repair
Bibliographic record
Abstract
Despite the advantages of laparoscopic inguinal hernia repair over the open approach in selected situations, laparoscopic inguinal hernia repair (LIHR) remains a challenge to teach and learn. The purpose of this study was to develop and validate a low-cost adaptable simulator for evaluation and training of LIHR. McGill Laparoscopic Inguinal Hernia Simulator (MLIHS) is a physical simulator that accurately represents inguinal anatomy. MLIHS permits to perform both transabdominal pre-peritoneal (TAPP) and totally extraperitoneal (TEP) repairs. Six experienced surgeons performed TAPP repairs using MLIHS. They were surveyed to establish face validity, and were scored using a previously validated global rating scale (maximum score = 25). Experienced surgeons considered MLIHS a useful tool for evaluating and training of LIHR. The mean (SD) global rating scores were 24.0 (± 0.6). The experienced surgeons considered MLIHS very useful for training and assessment. MLIHS is a valuable and affordable tool for training and evaluation of LIHR.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".