Assessment of laparoscopic skills before and after simulation training with a canine abdominal model
Bibliographic record
Abstract
OBJECTIVE-To determine whether scores for basic laparoscopic skills were significantly associated with extent of laparoscopic experience and compare basic laparoscopic skill scores obtained before and after 2 laparoscopic training sessions incorporating a canine abdominal model. DESIGN-Evaluation study. SAMPLE POPULATION-8 experienced and 25 novice individuals. PROCEDURES-Novice participants were randomly assigned to control (n = 10) and training (15) groups. Individuals in the experienced and novice training groups were required to undergo 2 training sessions with a canine abdominal model. Basic laparoscopic skills were assessed twice on the basis of 3 tasks included in the McGill Inanimate Simulator for Training and Evaluation of Laparoscopic Skills (MISTELS). RESULTS-For the novice training group, laparoscopic skills scores were significantly higher after training than before, but for individuals in the novice control group, scores did not differ significantly between the first and second assessments. The increase in score for the novice training group was significantly higher than increases for the experienced group and for the novice control group, but the increase in score for the experienced group was not significantly different from the increase in score for the novice control group. CONCLUSIONS AND CLINICAL RELEVANCE-Results suggested that basic laparoscopic skills scores obtained with the MISTELS were associated with extent of laparoscopic experience and that training with a canine abdominal model could increase skills scores for individuals without previous laparoscopic experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".