Assessing Image Quality of Low-Cost Laparoscopic Box Trainers: Options for Residents Training at Home
Bibliographic record
Abstract
INTRODUCTION: Low-cost laparoscopic box trainers built using home computers and webcams may provide residents with a useful tool for practice at home. This study set out to evaluate the image quality of low-cost laparoscopic box trainers compared with a commercially available model. METHODS: Five low-cost laparoscopic box trainers including the components listed were compared in random order to one commercially available box trainer: A (high-definition USB 2.0 webcam, PC laptop), B (Firewire webcam, Mac laptop), C (high-definition USB 2.0 webcam, Mac laptop), D (standard USB webcam, PC desktop), E (Firewire webcam, PC desktop), and F (the TRLCD03 3-DMEd Standard Minimally Invasive Training System). Participants observed still image quality and performed a peg transfer task using each box trainer. Participants rated still image quality, image quality with motion, and whether the box trainer had sufficient image quality to be useful for training. RESULTS: Sixteen residents in obstetrics and gynecology took part in the study. The box trainers showing no statistically significant difference from the commercially available model were A, B, C, D, and E for still image quality; A for image quality with motion; and A and B for usefulness of the simulator based on image quality. The cost of the box trainers A-E is approximately $100 to $160 each, not including a computer or laparoscopic instruments. CONCLUSIONS: Laparoscopic box trainers built from a high-definition USB 2.0 webcam with a PC (box trainer A) or from a Firewire webcam with a Mac (box trainer B) provide image quality comparable with a commercial standard.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".