Comparison of a single-access glove port with a SILS™ port in a surgical simulator model using MISTELS
Bibliographic record
Abstract
BACKGROUND: Recent advances in laparoscopy both in human and veterinary medicine have looked at means of being less invasive by using single-port access surgery as opposed to multiport access surgery. The glove port has gained popularity as a cost-effective alternative to commercially available single-port access devices. The primary aim of this study was to compare the glove port to the SILS™ port in a simulator model using the first two MISTELS (McGill inanimate system for training and evaluation of laparoscopic skills) tasks (peg transfer and pattern cutting). METHODS: Twenty-two novices were enrolled in this experimental study. Each participant had 60 min to practise both MISTELS tasks using two-port laparoscopy. Thereafter participants performed both tasks using the glove and SILS™ port with scores being calculated based on task completion time and errors. Higher scores were indicative of better performance. Participants were assigned into two groups with the starting order of the single ports being randomly selected. A self-evaluation questionnaire with three questions was completed by each participant after testing, rating each port. RESULTS: Significantly (p < 0.05) higher scores were achieved using the glove port compared to the SILS™ port when performing both tasks. The glove port was subjectively evaluated as easier to use with more manoeuvrability of the instruments than the SILS™ port. IMPLICATIONS OF THE STUDY: The glove port's improved manoeuvrability and ease of use make it a cost-effective alternative to the SILS™ port, for use in single-port laparoscopic veterinary surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".