Does contextual interference impact the retention of complex bimanual laparoscopic skills
Bibliographic record
Abstract
Laparoscopy simulation trainers have increased in accessibility which has encouraged endorsement of evidence-based training protocols. Yet, well-known motor learning effects may not extend to complex bimanual motor skills practiced with these devices. Contextual interference is a robust effect that is also feasible to implement in training. Blocked practice provides low contextual interference through repetition of the same skill over multiple trials before initiating a new skill. High contextual interference is produced through random practice where there is a random switching of practiced skills per trial. Blocked practice has been shown to increase skill performance during acquisition whereas random practice improves skill retention and transfer. We studied contextual interference learning effects during practice using a laparoscopy box trainer and a peg-transfer task. Participants completed fifty-four practice-trials using either blocked or random practice where they transferred pegs between two Maryland forceps to build one of three specified patterns. Participants undergoing blocked practice completed 18-trials of each pattern before proceeding to the next pattern. Participants undergoing random practice pseudo-randomly alternated between the three patterns. After a 10-min delay, all participants built each pattern once as a retention test. An identical retention test was performed after 10-days. Performance was measured by the time required to complete patterns with videos being used to determine duration in seconds. Time-to-completion in both the 10-min and 10-day retention tests did not significantly differ between those in either group. Our results thus far suggest high contextual interference practice schedules do not encourage retention of complex bimanual tasks.
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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 | 0.006 |
| 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) | 0.003 | 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".