Simulating Ovariohysterectomy: What Type of Practice Promotes Short- and Long-Term Skills Retention?
Bibliographic record
Abstract
Abstract Simulation-based surgical training allows students to learn skills through deliberate practice without the patient risk and stress of operating on a live animal. This study sought to determine the ideal distribution of training sessions to improve short- and long-term retention of the skills necessary to perform a simulated ovariohysterectomy (OVH). Fourth-semester students ( n = 102) were enrolled. Students in the weekly instruction group ( n = 57) completed 10 hours of training on the OVH simulator, with sessions held at approximately weekly intervals. Students in the monthly instruction group ( n = 45) completed the same training with approximately monthly sessions. All students were assessed 1 week (short-term retention test) and 5 months following the last training session (long-term retention test). Students in the weekly instruction group scored higher on their short-term assessment than students in the monthly instruction group ( p < .001). However, students’ scores in the weekly instruction group underwent a significant decrease between their short- and long-term assessments ( p < .001), while the monthly group did not experience a decrease in scores ( p < .001). There was no difference in long-term assessment scores between weekly and monthly instruction groups. These findings suggest that if educators are seeking maximal performance at a single time point, scheduling instructional sessions on a weekly basis prior to that time would be superior to monthly sessions, but if educators are concerned with long-term retention of skills, scheduling sessions on either a weekly or monthly basis would accomplish that purpose.
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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 | 0.002 |
| 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) | 0.001 | 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".