Bench Model Surgical Skill Training Improves Novice Ability to Multitask: A Randomized Controlled Study
Bibliographic record
Abstract
Skills training in simulation laboratories is becoming increasingly common. However, the educational benefit of these laboratories remains unclear. This study examined whether such training enables better performance on the simultaneous execution of technical skill and knowledge retention. Twenty-four novice trainees completed the elliptical excision on baseline testing. Following baseline testing twelve of the novices completed a technical practice (simulation training group) session, while the other twelve did not (control group). One week later, all participants returned for dual-task follow up testing in which they performed the excision while listening to a didactic lesson on the staging and treatment of cutaneous melanoma. The dual-tasking during the post test was standardized, whereby excision sutures 3 and 5 were performed alone (single), and sutures 4 and 6 were performed concurrently with the didactic lecture (dual). Seven additional trainees also participated as controls that were randomized to listen to the didactic lesson alone (knowledge retention alone group). Knowledge retention was assessed by a multiple choice questionnaire (MCQ). Technical performance was evaluated with computer and expert-based measures. Time to complete the performance improved among both groups completing the elliptical excision on follow-up testing (p<0.01). The simulation training group demonstrated superior hand motion performance on simultaneous didactic lesson testing (p<0.01). Novices from the no-training group performed statistically worse while suturing concurrently with the didactic lesson (p<0.01). The pretraining of novices in surgical skills laboratories leads to improved technical performance during periods of increased attention demands.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".